Human respiration feature extraction in personal emergency response systems and methods

ABSTRACT

A non-wearable Personal Emergency Response System (PERS) architecture is provided, implementing RF interferometry using synthetic aperture antenna arrays to derive ultra-wideband echo signals which are analyzed and then processed by a two-stage human state classifier and abnormal states pattern recognition. Systems and methods transmit ultra-wide band radio frequency signals at, and receive echo signals from, the environment, process the received echo signals to yield a range-bin-based slow signal that is spatially characterized over a plurality of spatial range bins, and estimate respiration parameter(s) of the human(s) by analyzing the slow signal. The antennas may be arranged in several linear baselines, implement virtual displacements, and may be set into multiple communicating sub-arrays. A classifier uses respiration and other derived features to classify the state of the human(s). A decision process is carried out based on the instantaneous human state (local decision) followed by abnormal states patterns recognition (global decision).

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 15/049,156 filed on Feb. 22, 2016, which in turn is acontinuation-in-part of and claimed priority from U.S. patentapplication Ser. No. 15/008,460 filed on Jan. 28, 2016, which in turn isa continuation-in-part of and claimed priority from U.S. patentapplication Ser. No. 14/983,632, filed on Dec. 30, 2015, which in turnis a continuation-in-part of and claimed priority from U.S. patentapplication Ser. No. 14/753,062, filed on Jun. 29, 2015, all of whichare incorporated herein by reference in their entireties.

FIELD OF THE INVENTION

The present invention relates to the field of elderly monitoring usingultra-wide band interferometry, and more particularly, to humanrespiration feature extraction in personal emergency response system(PERS).

BACKGROUND OF THE INVENTION

Elderly people have a high risk of falling, for example, in residentialenvironments. As most of elder people will need immediate help aftersuch a fall, it is crucial that these falls are monitored and addressedin real time. Specifically, one fifth of falling elders are admitted tohospital after staying on the floor for over one hour following a fall.The late admission increases the risk of dehydration, pressure ulcers,hypothermia and pneumonia. Acute falls lead to high psychologicaleffects of fear and negatively impact the quality of daily life.

Most of the existing personal emergency response systems (PERS), whichtake the form of fall detectors and alarm buttons, are wearable devices.These wearable devices have several disadvantages. First, they cannotrecognize the human body positioning and posture.

Second, they suffer from limited acceptance and use due to: elders'perception and image issues, high rate of false alarms and miss-detects,elders neglect re-wearing when getting out of bed or bath, and the factthat long term usage of wearable devices might lead to user skinirritations. Third, the wearable PERS are used mainly after experiencinga fall (very limited addressable market).

Therefore, there is a need for a paradigm shift toward automated andremote monitoring systems.

SUMMARY OF THE INVENTION

Some embodiments of the present invention provide a unique sensingsystem and a breakthrough for the supervision of the elderly duringtheir stay in the house, in general, and detect falls, in particular.The system may include: a UWB-RF Interferometer, Vector Quantizationbased Human states classifier, Cognitive situation analysis,communication unit and processing unit.

One aspect of the present invention provides a method comprising: (i)transmitting, via at least one transmitting antenna, ultra-wide band(UWB) radio frequency (RF) signals at an environment including at leastone human, and receiving, via at least one receiving antenna, echosignals from the environment, (ii) processing the received echo signalsto yield a range-bin-based slow signal that is spatially characterizedover a plurality of spatial range bins, (iii) estimating at least onerespiration feature of the at least one human by analyzing the slowsignal, and classifying the respiration feature(s) to indicaterespiration mode(s) of the human(s).

According to some embodiments of the present invention, the system maybe installed in the house's ceiling, and covers a typical elder'sapartment with a single sensor, using Ultra-Wideband RF technology. Itis a machine learning based solution that learns the elder's uniquecharacteristics (e.g., stature, gait and the like) and home primarylocations (e.g., bedroom, restroom, bathroom, kitchen, entry, etc.), aswell as the home external walls boundaries.

According to some embodiments of the present invention, the system mayautomatically detect and alert emergency situation that might beencountered by elders while being at home and identify the emergencysituations.

According to some embodiments of the present invention, the system maydetect falls of elderly people, but may also identify other emergenciessituations, such as labored breathing, sleep apnea, as well as otherabnormal cases, e.g., sedentary situation, repetitive non-acute fallsthat are not reported by the person. It is considered as a key elementfor the elderly connected smart home, and, by connecting the system tothe network and cloud, it can also make use of data analytics toidentify new patterns of emergencies and abnormal situations.

These, additional, and/or other aspects and/or advantages of the presentinvention are set forth in the detailed description which follows;possibly inferable from the detailed description; and/or learnable bypractice of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

The subject matter regarded as the invention is particularly pointed outand distinctly claimed in the concluding portion of the specification.The invention, however, both as to organization and method of operation,together with objects, features, and advantages thereof, may best beunderstood by reference to the following detailed description when readwith the accompanying drawings in which:

FIGS. 1A-1C are block diagrams illustrating a non-limiting exemplaryarchitecture of a system in accordance with embodiments of the presentinvention.

FIGS. 2A and 2B are high level schematic illustrations of configurationsof a linear baseline (SAAA), according to some embodiments of theinvention.

FIG. 2C illustrates a non-limiting example for image resolution dataachieved under the parameters defined above, for the various humanposture and ranges from the system, according to some embodiments of theinvention.

FIG. 2D schematically illustrates the dependency of image resolution onthe orientation of the object, according to some embodiments of theinvention.

FIGS. 2E-2G are high level schematic diagrams illustrating conceptual 2DSynthetic Aperture Antennas arrays with virtual displacements, accordingto some embodiments of the invention.

FIGS. 2H-2J are high level schematic illustrations of linear antennasarrays, according to some embodiments of the invention.

FIGS. 2K and 2L are simulation results that present the field of view ofthe array designs, according to some embodiments of the invention.

FIG. 2M shows simulation results that present the VSWR (Voltage StandingWave Ratio) with and without metal beams, or walls, according to someembodiments of the invention.

FIGS. 2N and 2O schematically illustrate an antenna array with tiltedbaselines, according to some embodiments of the invention.

FIG. 2P is high level schematic illustrations of conceptual 2D SyntheticAperture Antennas arrays providing unambiguous positioning, according tosome embodiments of the invention.

FIGS. 2Q and 2R illustrate the coverage of the system's surroundings inthe non-limiting case of four baselines, according to some embodimentsof the invention.

FIG. 2S is a high level schematic illustration of the system with twohome cells as a non-limiting example, according to some embodiments ofthe invention.

FIG. 3A is a high level schematic block diagram of the system whichschematically illustrates modules related to the posture extraction, inaccordance with embodiments of the present invention.

FIG. 3B is a high level schematic block diagram of the operationsperformed by a preprocessing unit, in accordance with embodiments of thepresent invention.

FIGS. 3C and 3D are illustrative examples for partially coherent images,according to some embodiments of the invention.

FIGS. 3E and 3F are illustrative examples for computed projections onthe x, y and z axes of 3D images of a person standing and laying,respectively, in front of the sensor according to some embodiments ofthe invention.

FIG. 3G illustrates schematically seven features on a schematic curverepresenting an arbitrary projection.

FIG. 3H is an illustration of an exemplary spanning of the features'space by two of the features described above, according to someembodiments of the invention.

FIG. 3I which is a schematic block diagram illustrating a trainingmodule in the posture classifier, according to some embodiments of theinvention.

FIG. 3J is a schematic block diagram of a classifying stage in theposture classifier, according to some embodiments of the invention.

FIG. 4A is a high-level schematic flowchart illustration of exemplarymotion feature extraction in feature extractor, according to someembodiments of the invention.

FIG. 4B is a high-level schematic illustration of fast and slow signalmapping, according to some embodiments of the invention.

FIG. 4C is a high-level schematic flowchart illustration of exemplaryhuman body target detection, according to some embodiments of theinvention.

FIG. 4D is a high-level schematic flowchart illustration of an exemplaryslow signal preprocessing unit, according to some embodiments of theinvention.

FIG. 4E is a high-level schematic flowchart illustration of exemplaryDoppler preprocessing and segmentation, according to some embodiments ofthe invention.

FIG. 4F is a high-level schematic flowchart illustration of an exemplarymaximal Doppler frequency extraction, according to some embodiments ofthe invention.

FIG. 4G is an exemplary illustration of a spectrogram of motion over asingle range bin in the active area, according to some embodiments ofthe invention.

FIG. 4H is a high-level schematic flowchart illustration of an exemplarymotion energy features extractor, according to some embodiments of theinvention.

FIG. 4I is a high-level schematic flowchart illustration of an exemplaryrange-time preprocessing and segmentation flow as part of derivation ofenergy signature, according to some embodiments of the invention.

FIG. 4J is a high-level schematic flowchart illustration of an exemplaryover-range energy distribution analysis as part of derivation of energysignature, according to some embodiments of the invention.

FIG. 4K is a high-level schematic flowchart illustration of an exemplaryover-range activity distribution analysis, according to some embodimentsof the invention.

FIG. 4L is a high-level schematic flowchart illustration of an exemplarymotion route energy estimation, according to some embodiments of theinvention.

FIG. 4M, being a schematic matrix illustration of DTW-based motion routeestimation, according to some embodiments of the invention.

FIG. 4N is a schematic illustration of the possibility to separatedifferent types of motions based on the derived parameters, according tosome embodiments of the invention.

FIG. 5A is a high level schematic illustration of a human respirationfeatures extraction system within the PERS system, according to someembodiments of the invention.

FIG. 5B is a high level schematic illustration of a motion rejectionfilter within the human respiration features extraction system,according to some embodiments of the invention.

FIG. 5C is a high level schematic illustration of a time to frequencyconverter within the human respiration features extraction system,according to some embodiments of the invention.

FIG. 5D illustrates in a non-limiting manner the product of apre-emphasis filter and the respiration spectrum, according to someembodiments of the invention.

FIG. 5E is a high level schematic illustration of a target localizationunit within the human respiration features extraction system, accordingto some embodiments of the invention.

FIG. 5F is a high level schematic exemplary illustration of ROIselection, according to some embodiments of the invention.

FIG. 5G is a high level schematic illustration of a range bins selectorproviding a range bin selection, within the human respiration featuresextraction system, according to some embodiments of the invention.

FIG. 5H is a high level schematic illustration of a rake combiner withphase shifting within the human respiration features extraction system,according to some embodiments of the invention.

FIGS. 5I and 5J provide an exemplary illustration of pre-accumulationphase shifted time signals, and of a post-accumulation respiration echosignal, respectively, according to some embodiments of the invention.

FIG. 5K is a high level schematic illustration of respiration featuresextraction from the time domain, within the human respiration featuresextraction system, according to some embodiments of the invention.

FIGS. 5L and 5M are high level schematic illustrations of a frameaverage power spectrum estimator and of a PCA-based past projection,respectively, which are used for respiration rate estimation, within thehuman respiration features extraction system, according to someembodiments of the invention.

FIG. 6A is a table illustrating an exemplary states definition inaccordance with some embodiments of the present invention.

FIG. 6B is a table illustrating an exemplary states matrix in accordancewith some embodiments of the present invention.

FIG. 6C is a table illustrating exemplary abnormal patterns inaccordance with some embodiments of the present invention.

FIG. 7 is a diagram illustrating a cloud-based architecture of thesystem in accordance with embodiments of the present invention.

FIG. 8 is a floor plan diagram illustrating initial monitored persontraining as well as the home environment and primary locations trainingin accordance with embodiments of the present invention.

FIG. 9 is a diagram illustrating yet another aspect in accordance withsome embodiments of the present invention.

FIG. 10 is a high level schematic flowchart of a method, according tosome embodiments of the invention.

It will be appreciated that for simplicity and clarity of illustration,elements shown in the figures have not necessarily been drawn to scale.For example, the dimensions of some of the elements may be exaggeratedrelative to other elements for clarity. Further, where consideredappropriate, reference numerals may be repeated among the figures toindicate corresponding or analogous elements.

DETAILED DESCRIPTION OF THE INVENTION

Prior to the detailed description being set forth, it may be helpful toset forth definitions of certain terms that will be used hereinafter.

The term “slow signal” as used in this application refers to the signalderived from received echo (fast) signals and is spatio-temporallycharacterized over multiple range bins (as spatial units) and multiplesub-frames (as temporal units).

The term “motion” as used in this application refers to the motion ofthe body and/or of body parts without displacement of the whole body asa bulk, such as gestures, limb motions, posture changes such as sittingdown or standing up, gait (separated from the displacement), motionsuddenness (e.g., possible fall or collapse) etc.

The term “movement” as used in this application refers to thedisplacement of a person's body as a whole, irrespective of the motionof body parts such as the limbs. In certain embodiments, the term“movement” may be used to refer only to radial displacements and radialcomponents of displacement with respect to the antenna, whereastangential displacement may be discarded. In certain embodiments,tangential components of the displacement may be taken into account asmovements as well.

The terms “transmitting antenna” and “receiving antenna” as used in thisapplication refer are non-limiting in the sense that the system may beconfigured to transmit signals via antennas denoted below as receivingantennas and receive echo signals via antennas denoted below astransmitting antennas. It is known in the art that the terms“transmitting antenna” and “receiving antenna” are interchangeable inthe sense that the associated electronic circuitry may be configured toreverse their respective functions. System optimization may be carriedout to determine which antennas are to be operated as transmittingantennas and which as receiving antennas. For the sake of simplicityalone, most of the following description related to transmittingantennas as single antennas and to receiving antennas as baselines(linear arrangements of antennas). It is explicitly noted that receivingantennas may be single antennas and transmitting antennas may bebaselines, while maintaining the applicability and scope of theinvention as described below.

In the following description, various aspects of the present inventionare described. For purposes of explanation, specific configurations anddetails are set forth in order to provide a thorough understanding ofthe present invention. However, it will also be apparent to one skilledin the art that the present invention may be practiced without thespecific details presented herein. Furthermore, well known features mayhave been omitted or simplified in order not to obscure the presentinvention. With specific reference to the drawings, it is stressed thatthe particulars shown are by way of example and for purposes ofillustrative discussion of the present invention only, and are presentedin the cause of providing what is believed to be the most useful andreadily understood description of the principles and conceptual aspectsof the invention. In this regard, no attempt is made to show structuraldetails of the invention in more detail than is necessary for afundamental understanding of the invention, the description taken withthe drawings making apparent to those skilled in the art how the severalforms of the invention may be embodied in practice.

Before at least one embodiment of the invention is explained in detail,it is to be understood that the invention is not limited in itsapplication to the details of construction and the arrangement of thecomponents set forth in the following description or illustrated in thedrawings. The invention is applicable to other embodiments that may bepracticed or carried out in various ways as well as to combinations ofthe disclosed embodiments. Also, it is to be understood that thephraseology and terminology employed herein is for the purpose ofdescription and should not be regarded as limiting.

Unless specifically stated otherwise, as apparent from the followingdiscussions, it is appreciated that throughout the specificationdiscussions utilizing terms such as “processing”, “computing”,“calculating”, “determining”, “enhancing” or the like, refer to theaction and/or processes of a computer or computing system, or similarelectronic computing device, that manipulates and/or transforms datarepresented as physical, such as electronic, quantities within thecomputing system's registers and/or memories into other data similarlyrepresented as physical quantities within the computing system'smemories, registers or other such information storage, transmission ordisplay devices. Any of the disclosed modules or units may be at leastpartially implemented by a computer processor.

A sensing system is provided for the supervision and fall detection ofthe elderly during their stay in the house. The system combines anUWB-RF (ultra-wide band radio frequency) interferometer with avector-quantization-based human states classifier implementing cognitivesituation analysis. The UWB-RF interferometer may implement a syntheticaperture and the human states classifier may have two stages and employabnormal states pattern recognition. The system may be installed in thehouse's ceiling, and cover the area of a typical elder's apartment (<100sqm) with a single sensor, using ultra-wideband RF technology.

The system may use machine learning to learn the elder's uniquecharacteristics (e.g., body features, stature, gait etc.) and the homeenvironment, and uses a human state classifier to determine theinstantaneous human state based on various extracted features such ashuman posture, motion, location at the environment as well as humanrespiration. The system may automatically detect, identify and alertconcerning emergency situations (particularly falls) that might beencountered by elders while being at home and identifies the emergencysituations. The system detects falls as well as identifies otheremergency situations such as labor briefing, sedentary situations andother abnormal cases. The decision process may be done based on theinstantaneous human state (local decision) followed by abnormal statespatterns recognition (global decision). The system global decision(emergency alert) is communicated to the operator through thecommunication system and two-ways communication is enabled between themonitored person and the remote operator.

The system may comprise a communication sub-system to communicate withthe remote operator and centralized system for multiple users' dataanalysis. A centralized system (cloud) may receive data from distributedPERS systems to perform further analysis and upgrading the systems withupdated database (codebooks).

Advantageously, the system may be used as a key element for the elderlyconnected smart home and by connecting the system to the network andcloud, it can also make a use of big data analytics to identify newpatterns of emergencies and abnormal situations. The system overcomesthe disadvantages of existing PERS such as wearable fall detectors andalarm buttons, as well as visual surveillance, by recognizing the humanbody positioning and posture and provides a significant enhancement inacceptability as it overcomes (i) elders' perception and image issues,(ii) high rate of false alarms and misdetections, (iii) elders' neglectof re-wearing when getting out of bed or bath, and (iv) user skinirritations by long term usage of wearable devices. Moreover, it may beused to prevent the first experience of fall (after which the use ofwearable devices is first considered) and does not involve privacyissues that visual surveillance system arise.

FIGS. 1A-1C are block diagrams illustrating a non-limiting exemplaryarchitecture of a system 100 in accordance with some embodiments of thepresent invention. As illustrated in FIG. 1A, system 100 may include aradio frequency (RF) interferometer 120 configured to transmit signalsvia Tx antenna 101 and receive echo signals via array 110-1 to 110-N. Txantennas 101 and Rx antennas 110 are part of an antenna array 115. Itshould be noted that transmit antennas and receive antennas may takedifferent forms, and, according to a preferred embodiment, in eachantenna array they may be a single transmit antenna and several receiveantennas. An environmental clutter cancellation module may or may not beused to filter out static non-human related echo signals. System 100 mayinclude a human state feature extractor 130 configured to extract fromthe filtered echo signals, a quantified representation of positionpostures, movements, motions and breathing of at least one human locatedwithin the specified area. A human state classifier may be configured toidentify a most probable fit of human current state that represents anactual human instantaneous status. System 100 may include an abnormalitysituation pattern recognition module 140 configured to apply a patternrecognition based decision function to the identified states patternsand to determine whether an abnormal physical event has occurred to theat least one human in the specified area. A communication system 150 forcommunicating with a remote server and end-user equipment for alerting(not shown here). Communication system 150 may further include two-waycommunication system between the caregiver and the monitored person forreal-time assistance.

As illustrated in FIG. 1B, system 100 comprises a system controller 105,a UWB-RF interferometry unit 220, a human state classifier 250, acognitive situation analysis module 260 and communication unit 150, theoperation of which is explained below (see FIG. 1C). UWB-RFinterferometry unit 220 comprises a UWB pulse generator 221, a UWB RFtransmission module 121, UWB transmitting antennas 101 that deliver aUWB RF signal 91 to an environment 80, e.g., one including at least onehuman 90, UWB receiver antennas 110 that receive echo signals 99 fromthe scene and UWB RF interferometer 120 that processes the received echosignals and provide signals for extraction of multiple features, asexplained below. Tx antennas 101 and Rx antennas 110 are part of antennaarray 115.

FIG. 1C is another block diagram illustrating the architecture of system100 in further details in accordance with some embodiments of thepresent invention as follows. UWB-RF interferometry unit 220 transmitsan ultra-wideband signal (e.g., pulse) into the monitored environmentand receives back the echo signals from multiple antenna arrays toprovide a better spatial resolution by using the Synthetic AntennaAperture approach. For example, UWB-RF interferometry unit 220 maycomprise transmission path pulse generator 221, UWB-RF front end 223connected to transmitting antenna(s) 101 and receiving antennas 110-1 .. . 110-N, e.g., arranged in arrays, and configured to transmit UWB RFsignals generated by generator 221 to the environment and deliver echopulses received therefrom to a reception path pro-processing module 222,possible implementing clutter cancellation with respect to clutteroriginating from the environment and not from human(s) in theenvironment. In order to increase the received signal-to-noise (SNR),the transmitter sends multiple UWB pulses and receiver receives andintegrates multiple echo signals (processing gain). The multiplereceived signals (one signal per each Rx Antenna) are sampled anddigitally stored for further signal processing.

Environmental clutter cancellation 230 may be part of a processing unit225 as illustrated and/or may be part of UWB-RF interferometry unit 220,e.g., clutter cancellation may be at least partially carried out by a Rxpath pre-processing unit 222. The echo signals are pre-processed toreduce the environmental clutter (the unwanted reflected echo componentsthat are arrived from the home walls, furniture, etc.). The outputsignal mostly contains only the echo components that reflected back fromthe monitored human body. Environmental clutter cancellation 230 is fedwith the trained environmental parameters 232. In addition, the cluttercancellation includes a stationary environment detection (i.e., no humanbody at zone) to retrain the reference environmental clutter for doorsor furniture movement cases.

The environmental clutter cancellation is required to remove unwantedecho components that are reflected from the apartment's static items,such as walls, doors, furniture, etc. The clutter cancellation is doneby subtracting the unwanted environmental clutter from the received echosignals. The residual clutter represents the reflected echo signals fromthe monitored human body. According to some embodiments of the presentinvention, the clutter cancellation also includes stationary environmentdetection to detect if no person is at the environment, such as when theperson is not at home, or is not at the estimated zone. Therefore, aperiodic stationary clutter check is carried out, and new referenceclutter fingerprint is captured when the environment is identified asstationary. The system according to some embodiments of the presentinvention re-estimates the environmental clutter to overcome the clutterchanges due to doors or furniture movements.

Feature extractor 240 that processes the “cleaned” echo signals toextract the set of features that will be used to classify theinstantaneous state of the monitored human person (e.g., posture,location, motion, movement, breathing, see more details below). The setof the extracted features constructs the feature vector that is theinput for the classifier.

Human state classifier 250—The features vector is entered to a VectorQuantization based classifier that classifies the instantaneous featuresvector by statistically finding the closest pre-trained state out of aset of N possible states, i.e., finding the closest code vector(centroid) out of all code vectors in a codebook 234. The classifieroutput is the most probable states with its relative probability (localdecision).

Cognitive Situation Analysis (CSA) module 260—This unit recognizeswhether the monitored person is in an emergency or abnormal situation.This unit is based on a pattern recognition engine (e.g., Hidden MarkovModel—HMM, based). The instantaneous states with their probabilities arestreamed in and the CSA search for states patterns that are tagged asemergency or abnormal patterns, such as a fall. These predefinedpatterns are stored in a patterns codebook 234. In case that CSArecognizes such a pattern, it will send an alarm notification to thehealthcare center or family care giver through the communication unit(e.g., Wi-Fi or cellular). Two-way voice/video communication unit150—this unit may be activated by the remote caregiver to communicatewith the monitored person when necessary. UWB-RF interferometry unit 220may include the following blocks: (i) Two-Dimensional UWB antenna array110-1-110-N to generate the synthetic aperture through all directions,followed by antenna selector. (ii) UWB pulse generator and Tx RF chainto transmit the pulse to the monitored environment UWB Rx chain toreceive the echo signals from the antenna array followed by analog todigital converter (ADC). The sampled signals (from each antenna) arestored in the memory, such as SRAM or DRAM.

In order to increase the received SNR, the RF interferometer may repeatthe pulse transmission and echo signal reception per each antenna (ofthe antenna array) and coherently integrate the digital signal toimprove the SNR.

Antenna Array and Interferometer

In order to successfully classify the human posture (based on thereceived echo signals) from any home location, an optimized2-Dimensional (2D) switched antenna array with a very wide field of view(FOV) was designed to generate the 3-dimensional (3D) back-projectionimage with a small, or even with a minimal number of antennas. In orderto cover the complete home environment, it may be split into severalhome cells, each with an installed system that detects and tracks themonitored person through the home environment. Coverage andinfrastructure consideration may be used to determine the exact systemconfiguration at different home environment. When the monitored personmoves from one home cell to another, a pre-defined set of criteria maybe used to determine whether to hand-over the human tracking from onecell to another. The width of the antenna array FOV may be configured toreduce the number of home cells while maintain the system's efficiencyand reliability.

FIGS. 2A and 2B are high level schematic illustrations of configurationsof a linear baseline (SAAA) 110, according to some embodiments of theinvention. FIG. 2A schematically illustrates an inline configurationwith individual elements separated by D/2 and a staggered configurationwith two lines of alternating elements separated by D/2 (on each lineelements are separated by D). FIG. 2B schematically illustrates somemore details of linear baseline 110. FIG. 2C illustrates a non-limitingexample for image resolution data achieved under the parameters definedabove, for the various human posture and ranges from system 100,according to some embodiments of the invention. FIG. 2D schematicallyillustrates the dependency of image resolution on the orientation of theobject, according to some embodiments of the invention.

The human posture may be determined by analyzing and classifying the3-dimensional human image as reconstructed by the back-projectionfunction based on the received echo signals (see above). The imageresolution is determined by the interferometer's Down Range (the imageresolution in the interferometer's radial direction—ΔR_(dr)) and CrossRange (the image resolution in the interferometer's angulardirection—ΔR_(cr)), with ΔR_(dr) determined by the transmitted pulsewidth and ΔR_(cr) determined by the Antenna Aperture and the range fromthe interferometer. In order to increase the antenna aperture, aSynthetic Aperture Antenna Array (SAAA) approach may be used by aswitched antenna array. Every SAAA is termed herein a Baseline.

The resolutions for SAAA (Baseline) 110 is given by ΔR_(dr)=c/2 B.W. andΔR_(cr)=λR/S.A. with c being the speed of light, B.W. being the pulsebandwidth, λ being the wave length, R being the range from the system'santenna 110, and S.A. being the synthetic aperture. ΔR_(dr) and ΔR_(cr)are selected to ensure that classifier 250 can recognize the humanposture. As a non-limiting example, the following parameter ranges maybe used: B.W. between 1 and 3 GHz (in a non-limiting example, B.W.=1.5GHz), λ between 0.03 m and 0.1 m (in a non-limiting example, λ=0.06 m),f between 3 and 9 GHz (in a non-limiting example, f=5 GHz), S.A. between0.1 m and 0.7 m (in a non-limiting example, S.A.=0.33 m), N_(antennas)between 3 and 21 antennas per baseline (in a non-limiting example,N=12), Antenna spacing between 0.03 m and 0.1 m (in a non-limitingexample, 0.03 m) with respect to scene parameters: Ceiling height=2.5 m,sitting person height=1 m, standing person height=1.5 m. Terminatedantennas are shown as elements that regulate the operation of the lastreceiver antennas 110-1 and 110-N in the row.

FIG. 2C presents image downrange and cross-range resolutions withrespect to the floor (assuming system 100 is mounted on the ceiling) toa sitting person, a standing person and laying person on floor. Thelinear baseline may be considered as a switched antenna array in aconstant spacing between each antenna element 110-1 . . . 110-N.Specific antenna elements may be selected through a control channel 102to perform the synthetic aperture.

FIG. 2D schematically illustrates the dependency of image resolution onthe orientation of the object, according to some embodiments of theinvention. The resolution is illustrated schematically by the size ofthe rectangles in the figure. As seen in FIG. 2C, the DownRange (DR)resolution is constant (depends on the bandwidth) while the CrossRange(CR) resolution depends on the antenna aperture and on the distance ofthe human from antenna array 110 of system 100.

FIGS. 2E-2G are high level schematic diagrams illustrating conceptual 2DSynthetic Aperture Antennas arrays 115 with virtual displacements,according to some embodiments of the invention. In FIG. 2E, antennaarray system 115 may include several linear arrays of antennas 110A,110B, 110C and 110D, as a non-limiting example Each row (linear antenna110A-D) may have a plurality of receive antennas 110-1 . . . 110-N asexplained above; and/or additional transmitting and/or receivingantennas may be part of array 115. As a non-limiting example, one ormore Tx antennas 101, 101A-D are illustrated at the central region ofarray 115. The solid line arrowed X marked 103 in FIG. 2E illustratesthe relative shifts of Tx antennas 101A-D with respect to Tx antenna101.

In FIG. 2F, 2D array structure 115 is shown with four baselines (lineararrays) 110A-D located along sides of a square. Tx antenna(s) 101 may beat the central region of 2D array structure 115. FIG. 2F illustratesschematically the effect of using virtual-displacement Tx Antennas101A-D as virtual movements of Rx baselines 110A-D in a samedisplacement vector (step and direction) as the moves from therespective virtual-displacement Tx Antenna 101A-D to the originalcentral Tx Antenna 101. The virtual displacements marked are denoted bybroken line arrowed X's marked 113. Virtual displacement of Tx antenna101 to 101A-D, e.g., by toggling between original central Tx antenna 101and any of virtual-displacement Tx Antennas 101A-D introduces additionalset of echo signals (Scatter) with different Radar Cross Section (RCS)from the target person with different signals' phases as a result of newroundtrip path from transmitting antenna, target, and receivingbaselines (antennas arrays). The additional diverse scatter (fouradditional echo signals sets) improves the reconstructed image in bothadditional processing gain (target reflection intensity) as well asadditional information due to the Tx antenna diversity.

It is emphasized that the indication of the transmitting antenna(s) asantenna elements 101 (and/or 101A-D) and the indication of the receivingbaseline(s) as antenna elements 110 (e.g., 110A-D) may be reversed,i.e., antenna elements 101 (and/or 101A-D) may be used as receivingantennas and antenna elements 110 (e.g., 110A-D) may be used astransmitting antennas. System 100 may be configured with receivingantennas 101 and transmitting antennas 110.

In FIG. 2G, 2D array structure 115 is shown with four linear arrays110A-D located along sides of a square and Tx antenna 101 at the centerof the square. Baseline arrays 110A-D may be virtually displaced (markedschematically by the gray arrowed X's) to yield additional virtualbaselines 113A-D to improve the back-projection image (see above) byincreasing the number of echo signals 99 with additional diversity.Virtual displacements of baseline arrays 110A-110D (FIG. 2G) may becombined with virtual displacements of Tx antenna 101 (FIG. 2F) as wellas with non-square positions (FIG. 2E) in any practical configuration tooptimize the antenna array configuration with respect to performance,size and cost.

UWB RF interferometer 120 may be to use multiple antennas to implementvirtual displacement of the baselines—either multiple antennas 101 arereceiving antennas and the virtual displaced baselines 110 aretransmitting baselines, or multiple antennas 101 are transmittingantennas and the virtual displaced baselines 110 are receivingbaselines.

FIGS. 2H-2J are high level schematic illustrations of linear antennasarrays 115, according to some embodiments of the invention. FIGS. 2K and2L are simulation results that present the FOV of the array designs,according to some embodiments of the invention. The simulations areelectromagnetic simulations at the E-Plane. As shown above, the majorrequirement from the linear antenna array for home environment is havinga large field of view, which becomes a real challenge for a UWB antennaarray. An innovated approach of widening the antenna array field of viewis presented herein. Exemplary implementations of UWB antenna element110 illustrated in FIGS. 2H-2J provide Field Of View (FoV) performancesthat are described in FIG. 2K (for the configuration of FIGS. 2H, 2I)and FIG. 2L (for the configuration of FIG. 2J) for a range of UWBfrequencies. FIG. 2J schematically illustrates the addition of (e.g.,two) metal beams 114 added along array 110 that widen the FOV, asillustrated in the simulation results in FIG. 2L (compare the wider FOVwith respect to FIG. 2K). FIG. 2M shows simulation results that presentthe VSWR (Voltage Standing Wave Ratio) with and without metal beams 114(=walls), according to some embodiments of the invention. FIG. 2Millustrates that metal walls 114 improve the antenna's VSWR at therelevant operation UWB band (4-6 GHz) with respect to an antenna lackingwalls 114.

In certain embodiments, a BALUN (Balance/Unbalance unit) may be locatedvertically below the antennas strip (e.g., one or more of baselines110).

FIGS. 2N and 2O schematically illustrate antenna array 115 with tiltedbaselines 110A-D, according to some embodiments of the invention.Baselines 110A-D may be tilted from their common plane, e.g., by a tiltangle α 112 ranging e.g., between 10-60°, so that, when antenna array115 is installed on a ceiling, baselines 110A-D do not face directlydownwards but somewhat sideways, by tilt angle α 112. The provided tiltprovides a larger field of view of antenna array 115 and hence system100. An optimization may be carried out involving as parameters e.g.,the antenna array unit vertical dimension (enabling the tilt), the fieldof view of the baselines and the array, and the degree of overlapbetween different baselines.

FIG. 2P is a high level schematic illustrations of conceptual 2DSynthetic Aperture Antennas arrays 115 providing unambiguouspositioning, according to some embodiments of the invention. Theseembodiments of non-limiting exemplary configurations enable to validatea location of a real target 90A by eliminating the possible images 95Aand 95B after checking reflections 99 received at correspondingsub-arrays of antennas 110A and 110D, respectively. It is wellunderstood that these configurations are non-limiting examples and otherantennas configurations may be used effectively. Any combinations ofembodiments of antenna arrays 115 illustrated herein are also consideredpart of the present invention. Two-dimensional array 115 guarantees thatecho signals 99 are received from any direction around array 115(assuming that each baseline 110A-D has a field of you of at least 120degrees), and as shown in the illustration, solves the directionambiguity of each individual baseline.

FIGS. 2Q and 2R illustrate the coverage of the system's surroundings inthe non-limiting case of four baselines 110A-D, according to someembodiments of the invention. In FIG. 2Q, the coverage 117A-D of eachbaseline 110A-D is illustrated alongside uncovered angular ranges116A-D. For the sake of clarity, single baseline 110 with coverageangular ranges 117 and uncovered angular ranges 116 is also illustrated.In this schematic non-limiting illustration, coverage angular ranges 117are considered as being within the primary beam of the baseline (−3 dB),between +60° and −60°. It is noted that wider or narrower definitionsmay be alternatively used with respect to the baseline and systemperformance and requirements.

FIG. 2R exemplify possible angular ranges 117A-D in degrees (relating to360° as the full circle coverage around array 115, i.e., 390°=30°) whichcover the whole range around array 115 with overlaps in baseline rangescovered by two baselines. The FoV is defined as the −3 dB points and maybe designed to cover 120° (±60°). Baselines 110 may be arranged to cover360° with respect to array 115 with a certain overlap between baselines110. Complementarily, baselines 110 may be arranged to solve the humantarget direction ambiguity by sufficient coverage and overlaprequirements. Similar consideration may be taken with respect to eitheror both primary and secondary beams.

FIG. 2S is a high level schematic illustration of system 100 with twohome cells 108A and 108B as a non-limiting example, according to someembodiments of the invention. In some houses/apartments environments 80,PERS system 100 may comprise more than one sub-systems 100A, 100B and/ormore than one antenna arrays 115A, 115B to cover whole environment 80effectively and to monitor target person 90 everywhere in environment80. For example, home environment 80 may be split into several homecells 80A, 80B, with respective sub-systems 100A, 100B and/or antennaarrays 115A, 115B that create respective sub-cells 108A, 108B.Sub-systems 100A, 100B, etc. may each comprise, e.g., a UWB RFinterferometry unit, a human state feature extractor and a human stateclassifier. Control unit 105 of system 100 regulates (e.g., according toa pre-defined set of criteria) hand-overs between sub-systems 100A, 100Band/or between antenna arrays 115A, 115B as monitored person 90 movesbetween home cells 108A, 108B, while maintaining continuous detectionand tracking. Examples for handing over criteria comprise: (i)BPI_(i)>BPI_(j) with BPI being the back-projection (accumulated)intensity from the monitored person as received at PERS, 100A andPERS_(j) 100B; and/or (ii) PDR_(i)<PDR_(j) with PDR being the persondown range distance from PERSi 100A and PERSj 100B as is estimated byeach PERS unit. Abnormality situation pattern recognition module 140 ofsystem 100 may be further configured to integrate input from allsub-systems 100A, 100B etc.

The multiple PERS sub-systems may hand-over person tracking amongthemselves by any of the following exemplary ways: (i) Hard hand-off:Once the handing over criteria are fulfilled by the releasing PERS unit,the person's tracking is moved from the releasing PERS unit which stopsthe tracking to the receiving PERS unit that starts tracking (breakbefore make); (ii) Soft Hand-off: Once the handing over criteria arefulfilled by the releasing PERS unit, the person's tracking is movedfrom the releasing PERS unit that keeps tracking the person and sendsthe information to the receiving PERS unit that starts tracking theperson. The realizing PERS unit stops tracking when the receiving PERSacknowledges that it successfully tracks the person (make before break);and (iii) Co-tracking: Each PERS sub-system that sufficiently identifiesthe person performs the tracking as long as the received scatter signaldoesn't decrease below a predefine threshold from the maximum receivedsignal among all the active PERS units. In this mode, the systemdecision is based on majority based voting between all the PERS units.

Multiple Features Extraction

Multiple features may be extracted y processing unit 225 from receivedecho signals by interferometer 120. For example, processing unit 225 maybe configured to process the received echo signals to derive a spatialdistribution of echo sources in the environment using spatial parametersof transmitting and/or receiving antennas 101, 110 respectively, withfeatures extractor 240 being configured to estimate a posture of atleast one human 10 by analyzing the spatial distribution with respect toecho intensity, as explained in detail below. For example, processingunit 225 may be configured to cancel environmental clutter by filteringout static non-human related echo signals, process the received echosignals by a back-projection algorithm, and analyze the spatialdistribution using curve characteristics of at least two projections ofan intensity of the received echo signals onto a vertical axis and atleast one horizontal axis, as explained below.

The “cleaned” echo signal vectors may be used as the raw data for thefeatures extraction unit. This unit extracts the features that mostlydescribe the instantaneous state of the monitored person. The followingare examples for the set of the extracted features and the method it'sextracted: Position—the position is extracted as the position (in caseof 2D—angle/range, in case of 3D—x, y, z coordinates) metrics output ofeach array baseline. The actual person position at home will bedetermined as a “finger print” method, i.e., the most proximity to thepre-trained home position matrices (centroids) codebook. Posture—theperson posture (sitting, standing, and laying) will be extracted bycreating the person “image” by using, e.g., a back-projection algorithm.Both position and posture are extracted, for example, by operating,e.g., the Back-projection algorithm on received echo signals—as acquiredfrom the multiple antennas array in SAR operational mode.

Human Posture

One aspect of the present invention provides a unique human posturesensing and classification system and a breakthrough for the supervisionof the elderly instantaneous status during their stay in the house, ingeneral, and extracting features of the human position and posture inparticular. The innovated system may be part of the Personal EmergencyResponse system (PERS) installed in the house's ceiling, and covers atypical elder's apartment (<100 sqm) with a single sensor. The innovatedsystem helps detecting and alerting an emergency situation that might beencountered by elders while being at home. The innovated system may alsoenable the long term monitoring of elderly activities and otherbehavioral tendencies during the staying at home.

The following is an outline of the procedure used to find the humanposition and posture, comprising the following steps: Dividing thesurveillance space into voxels (small cubes) in cross range, down rangeand height; Estimating the reflected EM signal from a specific voxel bythe back projection algorithm; Estimating the human position byaveraging the coordinates of the human reflecting voxels for eachbaseline (Synthetic Aperture Antenna Array); Triangulating allbaselines' position to generate the human position in the environment;Estimating the human posture by mapping the human related high-powervoxels into the form-factor vector; and Tracking the human movements inthe environment (bedroom, restroom, etc.).

FIG. 3A is a high level schematic block diagram of system 100 whichschematically illustrates modules related to the posture extraction, inaccordance with embodiments of the present invention. As explained indetail above, system 100 comprises UWB-RF interferometer 120 associatedwith antenna array 115 and delivering the received echo signals to homeenvironment clutter cancellation 230. The echo signals are thendelivered to a pre-processor 302, a human posture image back-projectionreconstruction module 310, possibly with floor enhancement 312 andprojection on the x, y, z axes 315 and finally to posture featuresextractor 240A (as part of feature extractor 240) and consequently toposture classifier 250A (as part of classifier 250) which derived aclassified posture 317, possibly using codebook 234.

Environmental clutter cancellation 230 may be configured to remove theunwanted echo components that are reflected from the apartment's staticitems as walls, door, furniture etc. The clutter cancellation may becarried out by subtracting the unwanted environmental clutter from thereceived echo signals. The residual clutter (scatter) represents thereflected echo signals from the monitored human body. System 100 may beconfigured to estimate (e.g., implementing a learning algorithm) theenvironmental clutter (to be cancelled) when there is no person at theenvironment, e.g., the person is not at home, or is not at the estimatedzone, and use the estimated clutter for clutter cancellation 230.Environmental clutter cancellation module 230 may comprise a stationaryenvironment detector that decided when the unit may re-estimate theenvironmental clutter, possibly with an addition manual control toperform the initial estimation during the system setup.

FIG. 3B is a high level schematic block diagram of the operationsperformed by preprocessing unit 302, in accordance with embodiments ofthe present invention. Preprocessing unit 302 may be configured toperform the following blocks for each of the received echo (fast)signals: DC removal 302A by continuously estimating the DC signal (timevarying DC). The estimated DC signal is subtracted from the originalsignal. Gain mismatch correction 302B may be performed to compensate forthe path loss differences among each of the interferometer's antennasreceived fast signals. Phase mismatch correction 302C may be performedto compensate for the time delay among the fast signals. An out of band(O.O.B.) noise reduction filter 302D (matched filter) may be configuredto filter out the out of pulse bandwidth noise and interferences.

Monitored the person's posture (e.g., sitting, standing, and laying) maybe extracted (240A) by creating the person's low resolution “image”,corresponding to a spatial distribution of echo sources, by usingback-projection algorithm 310. For example, position and posture may beextracted by operating back-projection algorithm 310 on received echosignals as acquired from the multiple antennas array in SyntheticAperture Antenna Array (SAAA) operational mode, illustrated in FIG. 2E.

For example, 3D back-projection may be formulated as indicated inEquations 1, by defining the locations of a J-transmitting antennaelements as the transmitting array (e.g., either of antennas 101 orantennas 110) and a K-receiving antenna elements as the receiving array(e.g., the other one of antennas 101 or antennas 110), expressing thereceived fast signals denoted J·K and deriving the absolute image valueI(V_(m)) using the confocal microwave imaging algorithm, applied to anyselected voxel V_(m) in the region of interest.

                                      Equations  1J − transmitting  antenna  elements  (transmitting  array)  located  at − {x_(tj), y_(tj), z_(tj)}_(j = 1)^(J)K − receiving  antenna  elements  (receiving  array)  located  at − {x_(rk), y_(rk), z_(rk)}_(k = 1)^(K)     J ⋅ K  received  fast  signals  are − {{s_(j, k)(t)}_(j = 1)^(J)}_(k = 1)^(K)     where  0 ≤ t ≤ T.  Voxel  V_(m) = (x_(m), y_(m), z_(m))$\mspace{20mu}{{I\left( V_{m} \right)} = {{\sum\limits_{j = 1}^{J}{\sum\limits_{k = 1}^{K}{{s_{j,k}\left( {t_{j,k}\left( V_{m} \right)} \right)}{\mathbb{e}}^{{j\varphi}_{j,k}{(V_{m})}}}}}}}$

The summation is over all the received fast signals s_(j,k)(t_(j,k)(V_(m))), and it contains the reflections equivalent to theround-trip, which is the total distance t_(j,k)(V_(m)) from each of thetransmitting antennas to the specific voxel V_(m) and the distance fromthis specific voxel V_(m) to each of the receiving antennas, ascalculated in Equations 2 in terms of the coordinates of thetransmitting and receiving arrays. The phase φ_(j,k)(V_(m)) is alsocalculated as presented in Equations 2. c denotes the speed of light andf_(c) denotes the central frequency.

$\begin{matrix}{{{t_{j,k}\left( V_{m} \right)} = \frac{{l_{j,m}\left( V_{m} \right)} + {l_{m,k}\left( V_{m}\; \right)}}{c}}{{l_{j,m}\left( V_{m} \right)} = \sqrt{\left( {x_{tj} - x_{m}} \right)^{2} + \left( {y_{tj} - y_{m}} \right)^{2} + \left( {z_{tj} - z_{m}} \right)^{2}}}{{l_{m,k}\left( V_{m} \right)} = \sqrt{\left( {x_{m} - x_{rk}} \right)^{2} + \left( {y_{m} - y_{rk}} \right)^{2} + \left( {z_{m} - z_{rk}} \right)^{2}}}{{\varphi_{j,k}\left( V_{m} \right)} = {\frac{2\pi\; f_{c}}{c}\left( {{l_{j,m}\left( V_{m} \right)} + {l_{m,k}\left( V_{m} \right)}} \right)}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$

The image, expressing the spatial distribution of the echo sources, maybe reconstructed from the absolute image values I(V_(m)) by computingthem for all the voxels in the region of interest Ω, i.e.,I(V_(m))=I(x_(m), y_(m), z_(m)), x_(m)ϵΩ_(x), y_(m)ϵΩ_(y), z_(m)ϵΩ_(z).This derived image is denoted in the following the “Coherent Image”, asit is a coherent accumulation of the fast signals' intensitycontributions from the Rx antennas. A “Partially Coherent Image”, whichis a more sophisticatedly-derived spatial distribution of the echosources, may be derived from several “2D Coherent Images” which are eachreconstructed from a subset of fast signals, and are then multiplied byeach other, as illustrated in Equations 3. Equations 3 relate as anon-limiting example to a single transmitting antenna (J=1) and 32receiving antennas (K=32) in four subsets (Baselines—BL_(i)). (e.g.,corresponding to central transmitting antenna 101 and receiving baseline110).“Partially Coherent Image”:I(V _(m))=Π_(i=1) ⁴ I _(i)(V _(m))“Coherent Images” (from subsets 1≤i≤4):I _(i)(V _(m))=|Σ_(j=1)^(i)Σ_(k=1) ⁸ s _(j,k)(t _(j,k)(V _(m)))e ^(jφ) ^(j,k) ^((V) ^(m) ⁾|subset 1: BL _(i) ={s _(1,1)(t),s _(1,2)(t), . . . ,s _(i,8)(t)}subset 2:BL ₂ ={s _(1,9)(t),s _(1,10)(t), . . . ,s _(1,16)(t)}subset 3:BL ₃ ={s _(1,17)(t),s _(1,18)(t), . . . ,s _(1,24)(t)}subset 4:BL ₄ ={s _(1,25)(t),s _(1,26)(t), . . . ,s_(1,32)(t)}  Equations 3

FIGS. 3C and 3D are illustrative examples for partially coherent images,according to some embodiments of the invention. FIGS. 3C and 3D arepartially coherent images of a standing person and a laying person,respectively. As seen in FIGS. 3C and 3D, the echo sources are detectedas a spatial distribution with a spatial resolution depending on thesizes of the voxels. The echo sources may be characterized, e.g., interms of human postures, according to the calculated and processedspatial distribution. High power voxels may be defined by a specifiedpower threshold, and used, possibly enhanced, to derive the posturefeatures.

Floor enhancement module 312 is configured to compute a floorenhancement 3D image, denoted I(X, Y, Z), from the Back-Projection 3Dimage generated by module 310. In the floor enhancement image theintensity is increased in the region of interest, e.g., the lower partof the 3D image that corresponds to the floor. In the process, the 3Dimage is divided into e.g., three levels: the lower cube level (floorregion), the intermediate (transition) region, and the upper level. Forexample, floor enhancement may be implemented multiplying the voxelintensity of floor region voxels by a factor greater than one, notaltering the upper level voxels, and multiplying the intermediate(transition) region voxels by a smoothing function, such as the functionexemplified, in a non-limiting manner, in Equation 4, with MaxWeightbeing the multiplication factor for floor region voxels and z being theheight above the floor.

$\begin{matrix}{{{FloorEnhancmentFuntion}(z)} = \left\{ \begin{matrix}{{{Max}\;{Weight}},} & {z < {50\lbrack{cm}\rbrack}} \\{{\frac{{{Max}\;{Weight}} - 1}{100 - 50}z},} & {{50\lbrack{cm}\rbrack} \leq z \leq {100\lbrack{cm}\rbrack}} \\{1,} & {z > {100\lbrack{cm}\rbrack}}\end{matrix} \right.} & {{Equation}\mspace{14mu} 4}\end{matrix}$

Module 315 is configured to perform 3D image projection on the x, y, zaxes, e.g., of the floor enhancement 3D image, by compressing the 3Dimage into three 1D signals for convenient processing. For this purpose,the projection of I(X, Y, Z) on axes x, y and z, denoted P_(x), P_(y)and P_(z), may be computed according to Equations 5. It is noted thatone or more projection axis may be used, e.g., a vertical axis and oneor more horizontal axes.

$\begin{matrix}{{P_{x}\overset{\Delta}{=}{{P\left( {X = x} \right)} = {\sum\limits_{y}{\sum\limits_{z}{{I\left( {{X = x},{Y = y},{Z = z}} \right)}{\forall{x \in \Omega_{x}}}}}}}}{P_{y}\overset{\Delta}{=}{{P\left( {Y = y} \right)} = {\sum\limits_{x}{\sum\limits_{z}{{I\left( {{X = x},{Y = y},{Z = z}} \right)}{\forall{y \in \Omega_{y}}}}}}}}{P_{z}\overset{\Delta}{=}{{P\left( {Z = z} \right)} = {\sum\limits_{x}{\sum\limits_{y}{{I\left( {{X = x},{Y = y},{Z = z}} \right)}{\forall{z \in \Omega_{z}}}}}}}}} & {{Equation}\mspace{14mu} 5}\end{matrix}$

FIGS. 3E and 3F are illustrative examples for computed projections onthe x, y and z axes of 3D images of a person standing and laying,respectively, in front of the sensor according to some embodiments ofthe invention. It is noted, e.g., that the z projection for the standingperson image (FIG. 3E) is typically different than the z projection forthe laying person image (FIG. 3F).

Various features may be computed for the three projections, P (i=x, y orz), such as: Standard Deviation(Pi), Kurtosis(Pi), Skewness(Pi),Max(Pi), Argmax(Pi), Min(Pi), Argmin(Pi), RightPosition(Pi),LeftPosition(Pi), Width(Pi), and so forth. The first three features arestatistical characteristics of the curves, namely their second, thirdand fourth standardized moments (centered moments) defined in Equations6, with p_(i) denoting the projections, x_(i) denoting the respectiveaxis points (p_(i)=P_(d)(x_(i)) with dϵ{X, Y, Z} and x_(i)ϵΩ_(x)) and pdenoting the average of p_(i) with N_(d) denoting the total samples peraxis, i.e. N_(x), N_(y) or N_(z). FIG. 3G illustrates schematicallyseven features on a schematic curve representing an arbitraryprojection. The features relating to the right and left of the curve maybe defined as being at an intensity below a specified threshold withrespect to the maximum, e.g., the threshold being between 5%-15% of themaximal intensity. The shorthand “arg” refers to the respective argument(axis value) and the width is defined between the right and leftpositions.

$\begin{matrix}{{\overset{\_}{p} = {\frac{1}{N_{d}}{\sum\limits_{i = 1}^{N_{d}}p_{i}}}}{{{Std}\left( P_{d} \right)} = \sqrt{\frac{1}{N_{d}}{\sum\limits_{i = 1}^{N_{d}}\left( {p_{i}^{2} - \overset{\_}{p}} \right)}}}{{{Skewness}\left( P_{d} \right)} = \frac{\frac{1}{N_{d}}{\sum\limits_{i = 1}^{N_{d}}\left( {p_{i} - \overset{\_}{p}} \right)^{3}}}{{{Std}\left( P_{d} \right)}^{3}}}{{{Kurtosis}\left( P_{d} \right)} = \frac{\frac{1}{N_{d}}{\sum\limits_{i = 1}^{N_{d}}\left( {p_{i} - \overset{\_}{p}} \right)^{4}}}{{{Std}\left( P_{d} \right)}^{4}}}} & {{Equation}\mspace{14mu} 6}\end{matrix}$

FIG. 3H is an illustration of an exemplary spanning of the features'space by two of the features described above, according to someembodiments of the invention. The features are seen to correlate withrespect to different posture of the person, such as standing, sittingand laying.

Returning to FIG. 3A, posture classifier 250A receives the extractedfeatures vector from posture features extractor 240A, the posturefeatures vector comprising the selected set of the features that wereextracted from the projections Px, Py and Pz. Classifier 250A isconfigured to determine whether the person is in a standing, sitting orlaying posture, for example according to the following example.

A set of all the possible postures is defined as {tilde over(C)}={posture₁, posture₂, . . . , posture_(c)} with c denoting the totalnumber of postures, for example, {tilde over (C)} may be a set ofpostures: {standing, sitting, laying}. X_(posture) _(i) is defined asthe set of template features vectors attributed to posture, and is usedto train the classifier and creating the codebook, as illustrated inFIG. 3I which is a schematic block diagram of a training module 251 inposture classifier 250A, according to some embodiments of the invention.The training phase of classifier 250A may comprise preprocessing 251A,configured to scale each feature, e.g., to have the variance in each ofthe features as a known constant and then, using a training process251B, creating codebook 234 (used later for the actual classification)of code-vectors, which projects the complete set of the various featuresvectors into a smaller subset. Training process 251B may be implementedby various methodologies, of which two are exemplified in the followingin a non-limiting manner. One example is a ‘Supervised VectorQuantization (VQ)’, in which codebooks 234 are created according to thenumber of postures, e.g., for C=3 postures, K centroids (centroids arethe centers of distributions according to a given measure, for examplen-dimensional means) may be defined per posture, resulting in 3·Kcentroids denoted as {{μ_(k,c)}_(k=1) ^(K)}_(c=1) ³. Another example isa One Codebook VQ, in which one codebook is created for all thepostures' feature vectors, without posture distinction. For example, Kcentroids may be defined for all the postures as {μ_(k)}_(k=1) ^(K).Moreover, for each centroid the internal distribution for each posture,denoted as the conditional probability of a posture given thecentroid—P(posture_(i)|centroid_(j)), may be determined. The priormatrix, expressed in Equation 7, is defined as having rows thatcorrespond to the centroids and columns that correspond to theprobability of each posture given this centroid. The classifying phase(250A) depends on the selected training methodology and resultingcodebook(s) 234, as exemplified below.

$\begin{matrix}{{PriorMatrix} = {\quad\begin{bmatrix}{P\left( {posture}_{1} \middle| {centroid}_{1} \right)} & \ldots & {P\left( {posture}_{C} \middle| {centroid}_{1} \right)} \\\vdots & \ddots & \vdots \\{P\left( {posture}_{1} \middle| {centroid}_{K} \right)} & \ldots & {P\left( {posture}_{C} \middle| {centroid}_{K} \right)}\end{bmatrix}}} & {{Equation}\mspace{14mu} 7}\end{matrix}$

FIG. 3J is a schematic block diagram of a classifying stage 252 inposture classifier 250A, according to some embodiments of the invention.In classifying phase 252, new feature vectors are entered into apreprocessing unit 252A and posture classifier 250A computes the bestposture fit out of all postures represented in codebook(s) 234, e.g., byusing a pre-defined cost function 252B. For example, cost function 252Bof the ‘Supervised VQ’ methodology may be defined as the minimumdistance across all the centroids. The classified posture is the postureattributed to the minimum distance centroid. In the second example, costfunction 252B of the One Codebook VQ methodology may be defined as inEquation 8, relating to the definitions of Equation 7, with x being thetested feature vector, P(x|entroid_(j)) calculated using the normaldistribution N(x|μ_(j),Σ_(j)) and P(centriod_(j)) estimated using thetotal vectors attributed to each of the centroids,{circumflex over (i)}=argmax_(i) P(posture_(i) |x)=argmax_(i)[Σ_(j)P(posture_(i),centroid_(j) |x)]=argmax_(i)[Σ_(j)P(posture_(i)|centroid_(j) ,x)P(centroid_(j) |x)]≅argmax_(i)[Σ_(j)P(posture_(i)|centroid_(j))P(centroid_(j) |x)]=argmax_(i)[Σ_(j)P(posture_(i)|centroid_(j))P(x|centroid_(j))P(centriod_(j))]   Equation8

Alternatively or complementarily, Support Vector Machine (SVM)classification may be implemented as posture classifier 250A, in whichthe features vectors are represented as linear lines that are formulatedas a set of cost functions. An unknown test vector is evaluated by thesecost functions and the classification is determined according itsresults.

Human Motion

Human motion—The monitored human body may create vibrations and othermotions (such as gestures and gait). Therefore, it introduces frequencymodulation on the returned echo signal. The modulation due to thesemotions is referred to as micro-Doppler (m-D) phenomena. The humanbody's motion feature may be extracted by estimating the micro-Dopplerfrequency shift vector at the target distance from the system (downrange). The following description and FIGS. 4A-4N elaborate on theaspect of human motion features extraction.

It is noted that the term “motion” refers to the motion of the bodyand/or of body parts without displacement of the whole body as a bulk,such as gestures, limb motions, posture changes such as sitting down orstanding up, gait (separated from the displacement), motion suddenness(e.g., possible fall or collapse), etc. The term “movement” refers tothe displacement of a person's body as a whole, irrespective of themotion of body parts such as the limbs (in case of movement detection bybackpropagation algorithms, the movement may comprise only the radialcomponents of displacement).

Non-wearable monitoring system 100 may comprise ultra-wide band (UWB)radio frequency (RF) interferometer 120 configured to transmit UWB RFsignals at, and to receive echo signals from, an environment includingat least one human, processing unit 225 configured to processing derive,e.g., at a slow signal derivation module 226, a range-bin-based slowsignal from the received echo signals, the slow signal beingspatio-temporally characterized over a plurality of spatial range binsand a plurality of temporal sub-frames, respectively, and featureextractor 240 configured to derive from the slow signal a Dopplersignature and a range-time energy signature as motion characteristics ofthe at least one human.

The Doppler signature may be derived by comparing spectral signatures ofsub-frames in the slow signals, which are related to identifyhuman-related range bins and sub-frames. The energy signature mayderived by evaluating powers of the slow signal at identifiedhuman-related range bins and sub-frames. The Doppler signature and/orthe energy signature may be derived with respect to different body partsof the at least one human.

Feature extractor 240 may be further configured to derive location datato yield movement characteristics of the at least one human. Thelocation data may be derived by detecting displacements of the at leastone human using back-projection and/or by identifying human-relatedrange bins and sub-frames in the slow signal. The derivation of thelocation data may be carried out using a spatio-temporal histogram ofthe range-time energy signature, by identifying on the histogram rangechanges of at least body parts of the at least one human.

System 100 may further comprise human state classifier 250 configured toclassify the motion and movement characteristics of the at least onehuman to indicate a state of the at least one human, and abnormalitysituation pattern recognition module 262, e.g., as part of cognitivesituation analysis module 260 configured to generate an alert once theindicated state is related to at least one specified emergency. Theclassification may carried out by identification of a most probable fitof one of a plurality of predefined states to the motion and movementcharacteristics and wherein the alert generation is based on patternrecognition with respect to previously indicated states.

FIG. 4A is a high-level schematic flowchart illustration of exemplaryhuman motion features extraction 241 in feature extractor 240, accordingto some embodiments of the invention. The Human Motion FeaturesExtractor system receives a UWB echo signal 401 and processes itaccording to the following blocks. Detailed descriptions of modules inFIG. 4A are presented in consecutive figures.

Echo (fast) signal preprocessing unit 405 receives the echo signals fromantennas 110-1 to 110-N. Each pulse transmission is represented by avector that is referred to in the following as the ‘fast time signal’.The transmission-reception cycle is performed repeatedly for a frame of,e.g., T_(frame)=2 to 5 seconds at a rate of, e.g., F_(slow)=100 Hz to300 Hz as non-limiting values. The output of unit 405 is a matrix of thereceived echo signals, where each row is a fast time signal of adifferent transmission.

Range bin based slow signal constructor (Fast2Slow) 410 rearranges thedownrange echo (fast) signals vectors (the matrix rows) to represent thecross-range (slow) signals 411 (the matrix columns), as illustrated inFIG. 4B below. The slow signal vector represents a single downrangedistance (bin) with a sampling rate, e.g., F_(slow)=100 Hz to 300 Hz asa non-limiting value. These vectors are referred as the ‘slow timesignals’.

Human body (target) detection is carried out by detecting itsrepresentation by a range bins window of e.g., RW_(target) ⁼50 to 200range bins (assuming, in a non-limiting manner, that each range bin isapproximately 1 cm), in a non-limiting example. The target location maybe determined by the range bins window with the highest motion poweramong all of the RW_(target) bins windows. The slow signal may bepreprocessed for each range bin separately and may include DC removal,which is done by the subtraction of the estimated average DC signal fromthe original signal as well as other optional signal adjustments forexample gain and phase mismatch correction between all the range binsslow signals and out-of-band noise reduction filtering.

Feature extraction 241 may be separated into two components—motionDoppler characteristics derivation 420A (motion Doppler features) andmotion change over range bins and time characteristics derivation 420B(motion energy features). Motion features extraction 241 yields a motionfeatures vector 440 which is then used for further processing andclassification in classifiers 130 and/or 250. The following demonstratesin a non-limiting manner possible embodiments of derivations 420A, 420B.

Motion characteristics detection 420 may comprise deriving from the slowsignal a Doppler signature, e.g., by block 420A, and a range-time energysignature, e.g., by block 420B, as motion characteristics of the atleast one human.

Motion characteristics detection 420 may comprise, concerning derivationof Doppler signature 420A, Doppler preprocessing and segmentation 422 inwhich the slow signal frame is divided into M_(subframes) sub-framesusing Equation 13 (see below). The spectrogram may be generated by fastFourier transform (FFT) for each slow time signal sub-frame within thehuman target range. A maximal Doppler frequency extractor 424 may usethe maximum Doppler frequency to identify the instantaneous moment andrange that a rapid motion (such as falling) has occurred. This featureis extracted by scanning all the slow time signal sub-frames per eachrange bin and accumulating the related power spectrum with the highestmotion (Doppler) frequency that is selected out of each range bin. Themaximal Doppler feature is extracted from the accumulated range binspower spectrums. A Motion Energy Extractor 426 may estimate the motionenergy features in the frequency domain. There are a few features thatare extracted to better represent the overall motion energy.

Motion characteristics detection 420 may comprise, concerning derivationof energy signature 420B, Range over Time preprocessing and segmentation432 in which the signal is preprocessed and segmentation of the datainto histograms is performed. For example, at a first stage, a DynamicTime Wrapping (DTW) process may be implemented to estimate the humanmotion path along the range bins window and at a second stage, e.g.,three histograms, which contain information about the distribution ofthe motion activity and energy signature over range, are generated torepresent: (i) Cumulated energy of all the range bins selected; (ii) Thenumbers of appearances of each range bin in the top 5 range bins; and(iii) The number of average energy for each range bin that appeared inthe top 5 ranges bins list. For each histogram, a set of features may beextracted to represent the histogram form factor, for example: (i)Motion energy distribution analysis 434 which comprises the extractionof features that represent the distribution of the energy over the rangebins, carried out e.g., by using the energy distribution histogramanalysis over range bins; (ii) Motion over range distribution analysis436 to represent the distribution of the active range bins during themotion period and helps determine if the motion is stationary in spaceor distributed among several range bins; and (iii) Motion route energyestimator 438 which extracts the motion route energy by accumulating thepower over the motion path (the selected range bins power as a result ofthe DTW at the pre-processing unit).

FIG. 4B is a high-level schematic illustration of fast and slow signalmapping 410, 411, according to some embodiments of the invention. Thereceived preprocessed fast signals are mapped in a two dimensionalmatrix X (Equation 9). Each echo sample is an element on the matrix[n][k]; n=1 . . . N_(Ranges); k=1 . . . K_(Samples), where n is thedownrange 1 bin indicator of spatial range bin, and k is the cross-range(slow) time indicator of temporal bins. The number of total range binsis determined by the scanning window, while each range bin representsC/F_(fast) meters (F_(fast) is the echo signal sampling rate). Thematrix is separated into its rows. Each row x_(n) is the echo signalfrom the same range from the interferometer (radar), sampled inF_(slow)=250 Hz. Those vectors are referred as the slow time signals.

$\begin{matrix}{{X\left( {{x\lbrack n\rbrack}\lbrack k\rbrack} \right)} = \begin{bmatrix}{{x\lbrack 1\rbrack}\lbrack 1\rbrack} & \ldots & {{x\lbrack 1\rbrack}\lbrack K\rbrack} \\\vdots & \ddots & \vdots \\{{x\lbrack N\rbrack}\lbrack 1\rbrack} & \ldots & {{x\lbrack N\rbrack}\lbrack K\rbrack}\end{bmatrix}} & {{Equation}\mspace{14mu} 9}\end{matrix}$

FIG. 4C is a high-level schematic flowchart illustration of exemplaryhuman body target detection 452, according to some embodiments of theinvention. Human Body Target Detection unit 452 narrows the focus of theanalysis to the relevant range bins with human presence. Unit 452 mayoperate with various inputs, according to the required features to beextracted. The process of the target detection given the slow timesignals of all the N range bins is performed by the following blocks, asan exemplary embodiment. A range bin power calculator 452A calculatesthe power of each slow time vector by Equation 10, where k and n are thetime and range bin indicators respectively, to yield N power values.p[n]=Σ _(k=1) ^(K) x _(n) ² [k] for n=1 . . . N _(ranges)  Equation 10Following, the power sequence over a sliding window of RW_(target) rangebins is calculated along the (N_(Ranges)−RW_(target)+1) windows (Eq.2.2) and accumulated by accumulator 452B, according to Equation 11.s[n]=Σ _(j=0) ^(M-1) p[j+n] for n=1 . . . (N _(Ranges) −RW_(target)+1)  Equation 11Finally, the human target location region is detected 452C and indicatedat the most powerful windowed power as expressed in Equation 12.Windicator=argmax_(n)(s[n])  Equation 12

FIG. 4D is a high-level schematic flowchart illustration of an exemplaryslow signal preprocessing unit 454, according to some embodiments of theinvention. It is noted that FIG. 4D is similar to FIG. 3B presentedabove, and is repeated here to maintain the flow of explanation in thepresent context. The signal processing itself may be similar or differin details with respect to posture features extraction. The slow timesignal preprocessing may be carried out in a generic unit having itsinput determined by the extracted features (e.g., of features vector440) and optionally operating on each slow time signal separately.Preprocessing unit 454 may perform the following blocks: (i) Adaptive DCremoval 454A by continuously calculating the estimated DC signal (timevarying DC) for each time bin by Equation 13, using the current slowsignal vector x[k],s[k]=(1−a)s[k−1]+ax[k], k=1 . . . K _(Samples)  Equation 13where a, is the learning coefficient. The estimated DC signal issubtracted from the original signal, namely y[k]=x[k]−s[k]. Gainmismatch correction 454B may optionally be performed to the selectedrange bins' slow signals to compensate the path losses differences amongthe selected range bins. The additional path loss of R_(i) versusR_(min) may be calculated as

${{\Delta\;{P.L.\lbrack{dB}\rbrack}} = {20\;{\log\left( \frac{R_{i}}{R_{\min}} \right)}}},$where R_(i) is the range bin i distance out of the selected set of rangebins and R_(min) is the first (closest) range bin. A slow signal phasemismatch correction 454C among the selected range bins may be carriedout to compensate for the motion offset over the time/range bin. Thatis, the same motion profile may be preserved between neighbor range binswith a delayed version. The slow signal phase mismatch correction mayestimate the phase error between SlowSig_(Ri) and SlowSig_(Rref), whereSlowSig_(Ri) is the slow signal of range bin R_(i), and SlowSig_(Rref)is the slow signal that is considered the reference range bin out of theselected range bins. Optionally, an out of band (O.O.B.) noise reductionfilter 454D may be enabled to filter out the irrelevant slow signalcomponents or interferences that might influence the performance of thevarious energy based features extraction.

FIG. 4E is a high-level schematic flowchart illustration of exemplaryDoppler preprocessing and segmentation 422, according to someembodiments of the invention. A spectrogram 422A for each range bin maybe generated and used for extraction of signal's spectral features, forevery short time period, termed herein sub-frame (e.g., a plurality ofspecific fast signal times, i.e., a range of k values). The sub-frameperiod should be short enough to consider the motion as stationary). Inorder for a spectrogram to be created from a slow time signal vector ofa specific range bin in the target region, slow time signal 411 of eachrange bin is first preprocessed by a preprocessing unit 422B, and then ahuman motion target detection unit 422C is being used to find the targetrange bin. Spectrogram 422A of each range bin is generated by segmentingthe original signal x[k] to M_(subframes) sub-vectors. For a givenwindow size and number of overlaps, a new vectors group is constructedaccording to Equation 14.{v _(m) [i]}={x[L _(step) m+i]}; i=1 . . . D; m=1 . . . M_(subframe)  Equation 14Each vector may have, as a non-limiting example, D_(WinSize) may bebetween 50 and 200 samples (equivalent a subframe length of between 0.15and 2 seconds) with overlaps of (D_(WinSize)−L_(step)) samples from theprevious vector in the sequence (L_(step)=is the samples step sizebetween subframes). Then, a power spectrum V_(m) may be computed foreach sub-frame by Equation 15, where h is a hamming window with lengthD.{V _(m)}=FFT{v _(m) ·h}; m=1 . . . M _(subframes)  Equation 15This process is repeated for every range bin within the target region.RW_(target) spectrograms 422D are gathered for further processing.

FIG. 4F is a high-level schematic flowchart illustration of an exemplarymaximal Doppler frequency extraction 424A, according to some embodimentsof the invention. Maximal Doppler frequency extractor 424 is configuredto find the highest velocity of the motion, which is represented by themotion's Doppler frequency along the motion's down-range route. Thetiming of the human peak motion activity is not common for all rangebins, due to the fact that the motions can cross range bins versus thetime. Therefore, the maximal Doppler frequency feature is extracted byscanning all the slow time signal sub-frames per each range bin andaccumulating the related power spectrum with the highest motion(Doppler) frequency that is selected out of each range bin. The maxDoppler feature may be extracted from the accumulated range bins powerspectrums. In order to extract the action power spectrum by extractor424B from each range bin spectrogram, the following process isperformed: Noise level threshold estimation computation 424C calculatesthe noise level threshold for the spectrogram energy by considering thespectrogram values below noise level are considered as not related tohuman motion. A threshold T₁ (measured in dB) may be determined byEquation 16, using the mean of the upper four frequency bins of thespectrogram, while Q, P are respectively the numbers of frequency andtime bins of the spectrogram matrix S.

$\begin{matrix}{{T_{1} = {\frac{1}{4}{\sum\limits_{q = {({Q - 4})}}^{q = Q}{\frac{1}{P}{\sum\limits_{p = 1}^{p = P}{s\left\lbrack {p,q} \right\rbrack}}}}}},{s \in S}} & {{Equation}\mspace{14mu} 16}\end{matrix}$The maximal motion frequency bin is defined and estimated in 424D, asthe first frequency bin to its power below the motion threshold whenscanning the spectrum from F_(min) to F_(max) which is the motion(active) region for the p power spectrum as defined by Equation 17.f _(p)=argmin_(q)(s[q,p]<(T ₁+1)) for p=1 . . . P  Equation 17where f_(p) is the maximal frequency at the p power spectrum that itspower is <T₁+1 dB. An example for that region from a full spectrogramcan be seen in spectrogram 424E of FIG. 4G.

FIG. 4G is an exemplary illustration of a spectrogram 424E of motionover a single range bin in the active area, according to someembodiments of the invention. Action power spectrum extractor 424Bfurther carries out a selection 424F of the power spectrum with thehighest frequency—the selected power spectrum at time bin p is the onethat has the highest value of f_(p) (referred as action power spectrumof range q). This power spectrum is extracted for farther analysis. Theaveraged action power spectrum P_(av) is created (424G) using actionpower spectrums 424E from all range bins. Then, a new noise threshold T₂is calculated from Equation 18, by using the average value of the upperfour frequency bins of the averaged (accumulated) power spectrums, in anon-limiting example.

$\begin{matrix}{T_{2} = {\frac{1}{4}{\sum\limits_{q = {({Q - 4})}}^{q = Q}{P_{av}\lbrack q\rbrack}}}} & {{Equation}\mspace{14mu} 18}\end{matrix}$The maximal frequency feature is calculated by Equation 19:f _(max)=argmin_(q)(P _(av) [q]<(T ₂+1))  Equation 19

FIG. 4H is a high-level schematic flowchart illustration of an exemplarymotion energy features extractor 426, according to some embodiments ofthe invention. The motion energy features may be estimated in thefrequency domain. There are a few features that are extracted to betterrepresent the overall motion energy. The motion energy might be affectedby several conditions which are not related to the motion itself. Forexample, the relative distance from the interferometer as well as themotion duration. Unit 426 may create several spectrograms for all thetarget range bins to extract the various features that represent theenergy signature.

The motion energy features may be extracted by the following exemplaryprocess. Two spectrogram versions may be created for each target rangebin. The first spectrogram may be created after a range gain mismatchcorrection (to compensate the path loss variations over the range bins).The other spectrogram may be created without the gain mismatchcorrection (426A). The gain mismatch may be implemented at preprocessingunit 422B. Therefore, two spectrogram sets are created for the completerange bins {S_(1n)} and {S_(2n)}. For each set of spectrograms, anaverage spectrogram 426B S_(1av) S_(2av) may be created by Equation 20.

$\begin{matrix}{{{{{S_{i,{av}}\lbrack q\rbrack}\lbrack m\rbrack} = {\frac{1}{RW}{\sum\limits_{n = 1}^{RW}{{S_{n}\lbrack q\rbrack}\lbrack m\rbrack}}}};}{{{for}\mspace{14mu} i} = 1},2,{m = {1\ldots\mspace{14mu} M_{subframes}}},{q = {1\ldots\mspace{14mu} Q_{freqbins}}}} & {{Equation}\mspace{14mu} 20}\end{matrix}$In order to emphasize the motion power in higher frequencies, eachaveraged spectrogram frequency bin {right arrow over (S)}_(av) ^(q) maybe processed with a corresponding weight, into a new weight-averagedspectrogram 426C by Equation 21.

$\begin{matrix}{{{{\overset{\rightarrow}{SW}}_{av}^{q} = {{\overset{\rightarrow}{S}}_{av}^{q}*\sqrt{\frac{f\lbrack q\rbrack}{f_{\max}}}}};}{{{for}\mspace{14mu} q} = {1\ldots\mspace{14mu} Q_{freqbins}}}} & {{Equation}\mspace{14mu} 21}\end{matrix}$where f[q] is the frequency value of the q frequency bin, and f_(max) isthe maximal frequency bin value. Two vectors of the power peaks may becreated (426D) for each the two spectrograms, with and without powercorrection. A first vector {right arrow over (p)}₁ contains the maximalpower of each sub-frame vector {right arrow over (s)}_(av) ^(m)(Equation 22A), and the second vector {right arrow over (p)}₂ containsthe maximal values of each frequency bin vector {right arrow over(s)}_(av) ^(q) (Equation 22B).p ₁ [m]=max({right arrow over (s)} _(av) ^(m)); for m=1 . . . M_(subframes)  Equation 22Ap ₂ [q]=max({right arrow over (s)} _(av) ^(q)); for q=1 . . . Q_(freqbins)  Equation 22BEach of the four (2×2) vectors—with the different procedures for gainprocessing and for maximal power values extraction, are accumulated intofour energy features.

FIG. 4I is a high-level schematic flowchart illustration of an exemplaryrange-time preprocessing and segmentation flow 432 as part of derivationof energy signature 420B, according to some embodiments of theinvention. The motion features over range-time helps profiling theenergy signature of each motion, not only by characterizing its powerand velocity, but by also characterizing its distribution over spacealong the motion time. Module 432 may be configured to create threehistograms that express the distribution of motion energy and activityover the range bins during motion period. The energy related histogramsmay be created by the following algorithms After normalization of theslow time matrix X (defined in Equation 9) using the highest absolutevalue amplitude as

$X = \frac{X}{{X}_{\infty}}$(the notation X is maintained for simplicity), the target region islocated by a target region detector unit 432A. The two axis of the slowtime matrix X correspond to the time of the sample (time bin) and therange of the sample (range bin). Each range bin vector, withK_(Samples)=T_(frame). F_(slow) length as an example, may then segmentedinto 10 sub-frames as a non-limiting example and mapped in as new matrixX_(n) defined in Equation 23, with K_(Samples)=800 as a non-limitingexample, with each row of the new matrix having K_(Samples)/10 sampleswith an overlap.

$\begin{matrix}{X_{n} = \begin{bmatrix}{x_{n}\lbrack 1\rbrack} & \ldots & {x_{n}\lbrack 80\rbrack} \\\vdots & \ddots & \vdots \\{x_{n}\lbrack 720\rbrack} & \ldots & {x_{n}\lbrack 800\rbrack}\end{bmatrix}} & {{Equation}\mspace{14mu} 23}\end{matrix}$With E_(n) being the temporal energy vector of each range bin ascalculated in Equation 24, j being the sub-frame number.

$\begin{matrix}{{E_{n}\lbrack j\rbrack} = {{\sum\limits_{i = 1}^{i = \frac{K}{10}}{{{{Xn}\left\lbrack {j,i} \right\rbrack}}^{2}\mspace{14mu}{for}\mspace{14mu} n}} = {1\ldots\mspace{14mu}{RW}_{target}}}} & {{Equation}\mspace{14mu} 24}\end{matrix}$A Matrix E defined by Equation 25 is constructed by gathering all thetemporal energy vectors from each range bin.

$\begin{matrix}{E = \begin{bmatrix}E_{1} \\\vdots \\E_{N}\end{bmatrix}} & {{Equation}\mspace{14mu} 25}\end{matrix}$The columns of E are the energies of all the ranges along the new widertime bins, and the rows are the energy of a specific bin along time.From each column with indicator k, the five highest elements values maybe extracted into w_(k)(r), r=1 . . . 5; together with their row indexesg_(k)(r), as a non-limiting example. The three histograms 432B arecreated from elements w_(k) (r) as defined by Equations 18A-C.An accumulated range histogram with elements calculated by Equation 26A:h _(acc)(n)=Σ_(k=1) ^(k=K) w _(k)(r)*I _((g) _(k) _((r)=n)) for n=1 . .. RW _(target)  Equation 26AThe indicator function I_((ωϵΩ)) is equal to 1 if the condition in thebrackets is true.An activity in range over time histogram, with elements calculated byEquation 26B:h _(app)(n)=Σ_(k=1) ^(k=K) I _((g) _(k) _((r)=n)) for n=1 . . . RW_(target)  Equation 26BA normalized energy histogram, with elements calculated by Equation 26C:

$\begin{matrix}{{h_{norm}(n)} = \left\{ {{\begin{matrix}{\frac{h_{acc}(n)}{h_{app}(n)},} & {{h_{app}(n)} > 0} \\{0,} & {{h_{app}(n)} = 0}\end{matrix}{for}\mspace{14mu} n} = {1\ldots\mspace{14mu}{RW}_{target}}} \right.} & {{Equation}\mspace{14mu} 26}\end{matrix}$

FIG. 4J is a high-level schematic flowchart illustration of an exemplaryrange energy distribution analysis 434 as part of derivation of energysignature 420B, according to some embodiments of the invention. Rangeenergy distribution analysis module 434 extracts features from theaccumulated and normalized energy histograms, which relate to the amountand distribution of motion energy over the range bins. Range energydistribution analysis 434 includes the extraction of the total andmaximal (peak) energy over the range bins out of the histogram. Inaddition, the histogram form factor, defined as the percentageaccumulated distribution points, is extracted (for exampleI20—identifies the range bin point that covers 20% of the accumulatedmotion energy, I40—identifies the range bin point that covers 40% of theaccumulated motion energy, etc.).

FIG. 4K is a high-level schematic flowchart illustration of an exemplarymotion over range distribution analysis 436, according to someembodiments of the invention. Motion over range distribution analysisunit 436 extracts features that relate to the distribution of activerange bins over time, which is related to the motion's and varying overthe down range. Unit 436 extracts the number of times that most ofactive region has been selected, the total number of active range binsand the mean number of repeated selection of the range bin as an activeregion.

FIG. 4L is a high-level schematic flowchart illustration of an exemplarymotion route energy estimation 438, according to some embodiments of theinvention. The motion route energy is defined as the accumulated poweralong the motion route in the range bins window during the motion period(time) relatively to the overall energy. This feature may be extractedin two major stages: (i) Estimating the motion route by using a dynamicTime Warping (DTW) approach 438A, (ii) accumulating the estimated poweralong the selected range bin route, and normalizing by the overall power438B and calculating the motion route peak to average power ratio 438C.The DTW may be performed by selecting the highest range bin power forevery sub-frame, as illustrated in FIG. 4M, being a schematic matrixillustration of DTW-based motion route estimation 438A, according tosome embodiments. The relative Motion Route Energy (MRE) may becalculated as expressed in Equation 27:

$\begin{matrix}{{M\; R\; E} = \frac{\sum\limits_{m = 1}^{Msubframes}{MP}_{\lbrack m\rbrack}}{\sum\limits_{m = 1}^{Msubframes}{\sum\limits_{r = 1}^{R}P_{\lbrack{m,r}\rbrack}}}} & {{Equation}\mspace{14mu} 27}\end{matrix}$Where:P_([m,r])=Σ_(n=1) ^(N)|x_(m,r[n])|² is the power of subframe m, andRange bin r;x_(m,r[n])—is the Slow signal at subframe m and range bin r; andMP[m]=max{P_([m,r])}, rϵWindow Range bins, is the Max Power at subframem.

The motion route Peak to Average Power Ratio (PAR), measured by theratio between maximal and average power of the motion route, may becalculated as in Equation 28:

$\begin{matrix}{{P\; A\; R} = \frac{\max_{m}\left( {{MP}\lbrack m\rbrack} \right)}{\frac{1}{Msubframes}{\sum\limits_{m = 1}^{Msubframes}{{MP}\lbrack m\rbrack}}}} & {{Equation}\mspace{14mu} 28}\end{matrix}$

FIG. 4N is a schematic illustration of the possibility to separatedifferent types of motions based on the derived parameters, according tosome embodiments of the invention.

The two illustrations in FIG. 4N are of the same 3D graphics and aretaken from different angles to illustrate the separation of the twotypes of motion in the 3D parameter space. FIG. 4N clearly illustratesthe ability of the analysis described above to separate motions that arecategorized, in the non-limiting illustrated case, as fall motions andas regular motions. The results may be used independently to detectfalls, or be provided to the classifier for verification andaugmentation with additional data and analysis results. Classificationof the human state, as described in detail below, may relate to thederived motion characteristics as well as optionally to posturecharacteristics, respiration characteristics and positioncharacteristics that may be derived from the received echo signals byimplementing the disclosed methods, approaches and/or additionalanalysis of the received echo signals.

Human Respiration

Human breathing—During the breathing (respiration) the chest wall movesby inhalation and exhalation. The average respiratory rate of a healthyadult is usually 12-20 breaths/min at rest (˜0.3 Hz) and 35-45breaths/min (˜0.75 Hz) during labored breathing. The respirationfeatures may be extracted from the slow-time signal (that is derivedfrom the received echo signals at the target's distance (down range)from the system) using spatial time parameters thereof and the powerspectrum as explained below. Specifically, in the following, the targetmay be selected as the chest of the one or more humans in theenvironment.

FIG. 5A is a high level schematic illustration of a human respirationfeatures extraction system 300 within system 100, according to someembodiments of the invention. Human respiration features extractionsystem 300 is configured to extract respiration features from thereceived echo signals, and may comprise the following modules andoperations. First, the echo signal may be rearranged by an echo (fast)signal preprocessing unit 405 configured to receive echo signals 401(99) from receiving antenna 110 with respect to each pulse transmission91, the received signals being represented by a vector termed the fasttime signal. The transmission-reception cycle may performed repeatedlyat any rate and duration, in an exemplary non-limiting manner, for aframe of T_(frame)=20 seconds and at a rate of F_(slow)=16 Hz. Theoutput of unit 405 may be a matrix of the received echo signals, whereeach row is a fast time signal of different transmission.

Fast to slow signal rearrangement and preprocessing (see also above) maycomprise a range bin based slow signal constructor and a slow signalpreprocessing unit. The range bin based slow signal constructor may beconfigured to rearrange the downrange echo (fast) signals vectors (thematrix rows) to represent the cross-range (slow) signals (the matrixcolumns)—see also FIG. 4B above. The slow signal vector represents asingle downrange distance (bin) with the sampling rate, e.g.,F_(slow)=16 Hz. These vectors are referred as the “slow time signals”and have a length of N_(samples). The slow signal preprocessing unit maycarry out the preprocessing for each range bin separately, comprisinge.g., DC removal by subtraction of the estimated average DC signal fromthe original signal as well as other optional signal adjustments forexample gain and phase mismatch correction between all the echo (fast)signals and out-of-band noise reduction filtering.

Then, a motion rejection filter 320 may be applied to estimate themotion interference for each range bin, e.g., by evaluating the signalLPC (linear predictive coding) coefficients for each sub-frame, e.g.,one second long sub-frames (see definitions above). Using the LPCcoefficients, the motion related components may be removed from each ofthe slow time signals, to enhance the slow time signal components whichcharacterize non-motion parameters such as respiration parameters.

A time-to-frequency constructor module 322 may be configured to convertthe slow time signal of each range bin into the corresponding powerspectrum, e.g., by applying an FFT (fast Fourier transform) with ahamming window, followed emphasizing higher respiration frequencies (seeFIGS. 5C and 5D below). Additionally, a target localization unit 324 maybe configured to select the target range bins, e.g., as the range binswith the highest respiration power spectrum energy within a Region ofInterest (ROI), e.g., relating to the chest of the respective humans.The ROI may be set by using the prior frames respiration energy overrange bins, and the current frame bins region that have high respirationenergy level (that can be related to the human general location), asillustrated e.g., in FIGS. 5E and 5F below. In particular, therespective chest(s) of the human(s) may be identified by detectingcross-correlation in the respiration-related ROI as chest movements arecorrelated in their motions over the corresponding range bins. Forexample, at least two coordinates (e.g., a start point and an end point)of the chest may be identified as the chest ROI.

Following the human chest target localization, a rake receiver 330 maybe configured to reconstruct the respiration signal, e.g., byimplementing a range bins selector 332 to compute the correlation ofeach range bin with the target range bin by using multiple variations ofphase shifts and selecting the N range bins that are most correlatedwith the target; and rake combiner 334 which shifts and combines theslow time signals of the selected range bins, by the range bins scanner,to reconstruct the respiration signal (see details in FIGS. 5G-5J).

The reconstructed respiration signal may be used to extract (338)respiration features 345 by calculating descriptive features of therespiration signal derivative. The features may focus on the respirationrate, asymmetry and other indicators of tidal volume changes. Forexample, a respiration rate 340 may be estimated by a frame averagepower spectrum estimator 336 that calculates the average power spectrumusing the power spectrums of each selected range bin, received fromrange bins combiner 334, PCA (principal component analysis) pastprojection module 326 that creates a PCA space based on the last,earlier-derived M frames and projects the average power spectrum overthe first principle component subspace to determine the respiration rateby the peak frequency of the projected average power spectrum. Arespiration rate estimator 340 then extracts the respiration rate fromthe past projected power spectrum. These and related aspects of system100 are presented below in detail.

Echo signal rearrangement unit 410, may be configured to map thereceived preprocessed fast signals (from module 405) in atwo-dimensional matrix X which is defined in Equation 29 (similar toEquation 9 above).

$\begin{matrix}\begin{bmatrix}{{x\lbrack 1\rbrack}\lbrack 1\rbrack} & \ldots & {{x\lbrack 1\rbrack}\lbrack N\rbrack} \\\vdots & \ddots & \vdots \\{{x\lbrack M\rbrack}\lbrack 1\rbrack} & \ldots & {{x\lbrack M\rbrack}\lbrack N\rbrack}\end{bmatrix} & {{Equation}\mspace{14mu} 29}\end{matrix}$

Each matrix element represents an echo sample, [m][n]; m=1 . . .M_(Ranges); n=1 . . . N_(Samples), where n denotes the downrange binindicator, and m denotes the cross-range (slow) time indicator. Thenumber of total range bins is determined by the scanning window, whileeach range bin represents C/F_(fast) meters (F_(fast) is the echo signalsampling rate). The matrix is separated into its rows (see FIG. 4B).Each row x_(m) is the echo signal from the same range from the radar,sampled e.g., in F_(slow)=16 Hz. Those vectors are referred as the slowtime signals. The slow time signal preprocessing unit 405 may beconfigured to be generic in the sense that its input is determined bythe extracted features and it may be operated on each slow time signalseparately.

FIG. 5B is a high level schematic illustration of motion rejectionfilter 320 within human respiration features extraction system 300,according to some embodiments of the invention. Following thepre-processing of slow time signal 401, motion rejection filter 320 isconfigured to remove motion related interferences, as described in thefollowing. The slow time signal x[n] of each range bin m is segmented(452) into sub frames v_(m)[n] of T_(SF)=1-2 second duration, asexpressed in Equation 30.{v _(k) ^(m) [n]}={x[D _(k) +n]}; n=1 . . . D; ∀m=1 . . . M _(ranges) ,k=1 . . . N_Samples/D _(subframesize)  Equation 30The motion interference model may then be estimated by anAuto-Regressive (AR) model 454, under the assumption that from a framewith the length of 1-2 second, only the motion spectrum components arepredicted, excluding the respiration frequency components. From each subframe the first K=2-4 Linear Prediction Coefficients (LPC) of each subframes are calculated. The estimated LPC coefficients of each sub-frameare then used to create a whitening filter and the correspondent filterof each sub frame is used to filter the motion interference 456. Thepost filtered sub frames of each range bin may be re-combined into afiltered signal 451.

FIG. 5C is a high level schematic illustration of time to frequencyconverter 322 within human respiration features extraction system 300,according to some embodiments of the invention. Time to frequencyconverter 322 is configured to enable the frequency analysis of thehuman breathing (which may range between 0.2 Hz and 0.75 Hz), e.g., byusing the slow time signal of each range bin to calculate therespiration power spectrum, as explained in the following. Each Slowtime x_(m) signal 401 may be processed by FFT with hamming window h 457,as expressed in Equation 31.{X _(m)}=FFT{x _(m) ·h}; ∀m=1 . . . M _(Ranges)  Equation 31The respiration power spectrum for each range bin may be represented bythe power spectrum in the frequencies bands of 0.2-1 Hz as expressed inEquation 32.X _(m) [k], k=1 . . . NF _(MAX) ∀m=1 . . . M _(Ranges)  Equation 32

A high respiration frequencies pre-emphasis 458 may be implemented byapplying a corresponding filter 458 to overcome an expected attenuationof the signal amplitude at high respiration rates due to the naturallimitation of the human maximal tidal volume when breathing at a highrespiration rate (because the amount of air that is inhaled is notsufficient to fill the lungs). The high frequencies pre-emphasis 458emphasizes the higher frequencies to balance the effect of the lowertidal volume on the power spectrum. An exemplary enhancement filter w isexpressed in Equation 33, with the parameter a relating to the rate ofmaximal tidal volume that results from increasing the respiration rate.

$\begin{matrix}{{w(f)} = \left\{ \begin{matrix}{1,} & {f < F_{MI}} \\{{1 + {\left( {f - F_{MI}} \right) \cdot \alpha}},} & {f \geq F_{MI}}\end{matrix} \right.} & {{Equation}\mspace{14mu} 33}\end{matrix}$As a non-limiting example α=3.

FIG. 5D illustrates in a non-limiting manner the frequency response ofthe pre-emphasis filter 458, according to some embodiments of theinvention. The operation of enhancement filter 458 on the respirationsignal is expressed in Equation 34, resulting in respiration frequencies341. X_(E)[f] denotes the enhanced respiration spectrum that compensatesthe non-full inhale/exhale situations in hyperventilation cases. F_(MI)denotes the Maximum Respiration Frequency with a full inhale/exhalecondition.X _(E)(f)=X(f)·w ²(f) for f=0 . . . F _(MaxResp)  Equation 34

FIG. 5E is a high level schematic illustration of target localizationunit 460 within human respiration features extraction system 300,according to some embodiments of the invention. Target localization unit460 may be configured to use the current and past distribution ofrespiration energy over range bins 461, in order to determine thetarget's location range bins (e.g., of the chest of the human(s) in thescene). Although there may be more than a one range bin that includesthe target, target localization unit 460 may be configured to identifyor select a specific range bin that can be used as a reference, in orderto locate other relevant range bins based on their similarity to theidentified or selected range bin.

First, the search for the respiration target range bin may be limited toa Region of Interest (ROI), which is estimated by using past targetregions, in order to reduce the chance of selecting a false range binhaving high energy which does not relate to the respiration component.FIG. 5F is a high level schematic exemplary illustration of ROIselection, according to some embodiments of the invention. The area withthe highest respiration energy within the ROI may be selected as thecenter of the ROI for searching the reference target range bin, e.g., asexplained below.

The respiration energy of each range bin may be estimated (462) byaccumulation of the respiration power spectrums as expressed in Equation35, which includes all the respiration frequencies bins—R_(bins)·E[m]denotes the respiration energy at bin m, X_(m)[k] denotes the powerspectrum of range bin m and frequency bin k, and F_(bins) denotes thenumber of the frequency bins.E[m]=ρ _(k=1) ^(F) ^(bins) X _(m) [k], ∀m=1 . . . M _(Ranges)  Equation35

Detecting ROI 465 may be carried out as follows (see also FIG. 5F). Themedian value of all the range bins energies E [m] may be calculated overthe different values of m, and the initial ROI range bin may bedetermined by the first range bin (from the sensor) with respirationenergy above the median value. Then, a weighted respiration energy maybe calculated by accumulating past range bins energies, according toEquation 36, where E_(j)[m] are the past average range bins energies(j=1 is the most recent while j=K is the oldest).

$\begin{matrix}{{E_{av}\lbrack m\rbrack} = \frac{\sum\limits_{j = 1}^{K}{{E_{j}\lbrack m\rbrack}\left( {K - j} \right)}}{K}} & {{Equation}\mspace{14mu} 36}\end{matrix}$

The range bin with highest past projected range bin may be selected tobe the middle of the ROI. The total ROI area may be determined asexplained below and exemplified in FIG. 5F—The ROI Start may be selectedas the first range bin with energy above median, the ROI Center may bedetermined as the estimated location by past frames, and the ROI End maybe selected as the range bin that has a correlation with the ROI Centerrange bin which exceeds a pre-defined threshold. Finally, the targetlocation may be determined as the range bin with the highest respirationpower within the ROI 466 (FIG. 5E). A range bins selector 468 may beconfigured to search for range bins that are highly correlated with thereference respiration target range bin slow signal, with a selection 469of the range bins being performed as illustrated in FIG. 5G.

FIG. 5G is a high level schematic illustration of range bins selector332 providing a range bin selection 469, within human respirationfeatures extraction system 300, according to some embodiments of theinvention. The tested range bins set, x_(m)[n], may be cross-correlatedwith the reference range bin R_(ref) slow-time signal x_(R)[n] 471 afterapplying a delay tap t (472), e.g., x_(m) ^(t)[n]=x_(m)[n−t], t=1 . . .T_(shifts)∀m=1 . . . M_(Ranges). Using the sliding window, multiplecross-correlations (e.g., T_(shifts)=32) may be produced for each rangebin slow time signal, from zero phase up to T_(shifts) taps, withine.g., two seconds time frame. Then, the range bins with the highestcorrelation may be selected 474, with the correspondent phase shiftbeing expressed by Equation 37

$\begin{matrix}{{{CC}\left\lbrack {m,t} \right\rbrack} = \frac{\sum\limits_{n = 1}^{N}{{x_{R}\lbrack n\rbrack}*{x_{m}^{t}\lbrack n\rbrack}}}{\sqrt{\sum\limits_{n = 1}^{N}{x_{R}\lbrack n\rbrack}^{2}}}} & {{Equation}\mspace{14mu} 37}\end{matrix}$

Range bins selector 469 may be configured to select N_(R) range bins 476with the maximal cross-correlation C[m] out of all range-bins that arerelated to the target (e.g., human chest), and the correspondent phaseshifts PS[m] that achieve these values. That is, selected range bins 476may be denoted as m′ϵm, m′=1 . . . N_(R) that are selected according toEquations 38.

$\begin{matrix}{{{{C\lbrack m\rbrack} = {\max\limits_{t = {1\mspace{11mu}\ldots\mspace{14mu} T_{shifts}}}\left\{ {{CC}\left\lbrack {m,t} \right\rbrack} \right\}}};}{{{PS}\lbrack m\rbrack} = {{argmax}_{t}\left\{ {{CC}\left\lbrack {m,t} \right\rbrack} \right\}}}} & {{Equations}\mspace{14mu} 38}\end{matrix}$

FIG. 5H is a high level schematic illustration of rake combiner 334 withphase shifting, within human respiration features extraction system 300,according to some embodiments of the invention. Each of slow timesignals 401 may be zero padded from both sides, shifted by itscorrespondent phase, and all the shifted signals are accumulated (478)into one signal as expressed in Equations 39.

$\begin{matrix}{{{{\hat{x}}_{m_{\;}^{\prime}}^{{PS}{\lbrack m^{\prime}\rbrack}}\lbrack n\rbrack} = {{ZerosPadding}\left\{ {x_{m^{\prime}}^{{PS}{\lbrack m^{\prime}\rbrack}}\lbrack n\rbrack} \right\}}}{{\hat{x}\lbrack n\rbrack} = {\sum\limits_{m^{\prime} = 1}^{R}{{\hat{x}}_{m^{\prime}}^{{PS}{\lbrack m^{\prime}\rbrack}}\lbrack n\rbrack}}}} & {{Equations}\mspace{14mu} 39}\end{matrix}$

After applying a rectangle window 479 to preserve the Nsamples framesize, the DC component of the combiner output signal may be estimated byan average estimator as the average signal dc[n]. The average signal maythen be removed (480) from the combined output signal to produce therespiration signal y[n] 482 by Equation 40.y[n]={circumflex over (x)}[n]−dc[n]  Equation 40

FIGS. 5I and 5J provide an exemplary illustration of pre-accumulationphase shifted time signals (476), and of post-accumulation respirationecho signal 482, respectively, according to some embodiments of theinvention.

FIG. 5K is a high level schematic illustration of respiration featuresextraction from time domain 338, within human respiration featuresextraction system 300, according to some embodiments of the invention.Respiration features 345 that are extracted from reconstructedrespiration signal 482 may be selected to provide information on thephysiological properties of the respiration process. These features areextracted from the time domain analysis of the slow time respirationsignal as explained in the following. A derivative 484 of respirationsignal 482 is calculated, and an absolute value 484A and a sign 484B ofrespiration signal derivative 484 are separated into two signals byEquations 41.y′[n]=y[n]−y[n−1], ∀n=2 . . . Ny ₁ [n]=|y′[n]|y ₂ [n]=sign(y′[n])  Equations 41

As a non-limiting example, six respiration features may be derived fromthe sign and absolute value of the respiration derivative, defined asF1-F6 in Equations 42, and used to construct respiration features vector345.

$\begin{matrix}{{{{F\; 1} = \frac{\sum\limits_{n = 1}^{N}1_{{y_{1}{\lbrack n\rbrack}} > {Threshold}}}{\sum\limits_{n = 1}^{N}1_{{y_{1}{\lbrack n\rbrack}} < {Threshold}}}};{{F\; 2} = \frac{\sum\limits_{n = 1}^{N}1_{{y_{2}{\lbrack n\rbrack}} = {{1\bigcap{y_{1}{\lbrack n\rbrack}}} > {Threshold}}}}{\sum\limits_{n = 1}^{N}1_{{y_{2}{\lbrack n\rbrack}} = {{{- 1}\bigcap{y_{1}{\lbrack n\rbrack}}} > {Threshold}}}}};}\mspace{20mu}{{F\; 3} = {{med}_{n}\left( y^{\prime} \right)}}{{F\; 4} = {\max\left( {\frac{\sum\limits_{n = 1}^{N}{{y_{1}\lbrack n\rbrack} \cdot 1_{{y_{2}{\lbrack n\rbrack}} = {{1\bigcap{y_{1}{\lbrack n\rbrack}}} > {Threshold}}}}}{\sum\limits_{n = 1}^{N}1_{{y_{2}{\lbrack n\rbrack}} = {{1\bigcap{y_{1}{\lbrack n\rbrack}}} > {Threshold}}}},\frac{\sum\limits_{n = 1}^{N}{{y_{1}\lbrack n\rbrack} \cdot 1_{{y_{2}{\lbrack n\rbrack}} = {{1\bigcap{y_{1}{\lbrack n\rbrack}}} < {Threshold}}}}}{\sum\limits_{n = 1}^{N}1_{{y_{2}{\lbrack n\rbrack}} = {{1\bigcap{y_{1}{\lbrack n\rbrack}}} < {Threshold}}}}} \right)}}{{F\; 5} = {\max\left( {{\sum\limits_{n = 1}^{N}{{y_{1}\lbrack n\rbrack} \cdot 1_{{y_{2}{\lbrack n\rbrack}} = {{1\bigcap{y_{1}{\lbrack n\rbrack}}} > {Threshold}}}}},{\sum\limits_{n = 1}^{N}{{y_{1}\lbrack n\rbrack} \cdot 1_{{y_{2}{\lbrack n\rbrack}} = {{1\bigcap{y_{1}{\lbrack n\rbrack}}} < {Threshold}}}}}} \right)}}{{F\; 6} = {\frac{1}{J}{\sum\limits_{j = 1}^{J}{\max\limits_{n \in I_{j}}{y_{1}\lbrack n\rbrack}}}}}} & {{Equation}\mspace{14mu} 42}\end{matrix}$

In this example, F1 denotes the ratio between samples of the absolutederivative value that pass the pre-defined threshold versus the onesthat do not pass that threshold. F1 may be used to provide informationon the breath's duty cycle. F2 denotes the ratio between the inhalationand exhalation periods. F2 may be used to provide information on therespiration asymmetry, in terms of inhalation time versus exhalationtime. F3 denotes the median value of the entire derivative signal. F3may be used to provide information on the amount of chest movementswhich indicate the amount energy related to the respiration action. F3is expected to increase when the person is under stress. F4 denotes themaximum value of the average inhalation—exhalation and may be calculatedby the average values of the derivative absolute value samples that passthe threshold and located at ascending or descending regionsrespectively. F4 may be used to provide information on the average chestvelocity, which can be related to the average amount of air inhaled andexhaled F5 denotes the maximum value of the total inhalation—exhalation,and may be calculated by the total values of the derivative absolutevalue samples that pass the threshold and are located as ascending ordescending regions respectively. F5 may be used to provide informationon the total chest activity, which can be related to the total amount ofair inhaled and exhaled. F6 denotes the averaged inhalation/exhalationpeaks within each frame segment, with J representing the number ofsegments, e.g., J may be in a range of 3 to 5 segments of 3-5 secondeach. F6 may be used to provide information about the maximum peak rateof inhalation-exhalation.

FIGS. 5L and 5M are high level schematic illustrations of frame averagepower spectrum estimator 336 and of Principle Component Analysis(PCA)-based respiration rate estimator 326, respectively, that are usedfor respiration rate estimation 340, within human respiration featuresextraction system 300, according to some embodiments of the invention.The power of the range bins selected (474) by range bins scanner 332 maybe combined to yield an average power spectrum 488 of the respirationprocess and each of the N_(R) range bins may be accumulated into a jointaverage spectrum of the respiration 490, as expressed in Equation 43.

$\begin{matrix}{{{{Pav}\lbrack k\rbrack} = {\frac{1}{N_{R}}{\sum\limits_{r = 1}^{N_{R}}{P_{{RB}_{r}}\lbrack k\rbrack}}}},} & {{Equation}\mspace{14mu} 43}\end{matrix}$where:P_(RB) _(r) [k] denotes the power spectrum of slow time signal atrange-bin RB_(r), andN_(R) denotes the number of the selected range bins to be averaged inthe power spectrum.

Respiration rate estimation based on PCA (326) may be configured toreduce the effect of unwanted components such as the noise and the lowmotions interference signature, under the assumption that therespiration power spectrum usually fluctuates over the consecutiveframes versus noise and quasi-static motion components. The PCA-basedrespiration rate estimation 326 may be configured to extract the firstprinciple component 492 out of a current PCA space 494 that is generatedfrom the L last frames' power spectra and the current frame powerspectrum. The estimated respiration rate 340 may then be identified asthe argument (frequency bin) 496 of the maximum value of the extractedfirst principle component vector.

The features vector relating, e.g., to posture, motion and respirationfeatures and/or respiration modes, derived as described above, mayprepared by quantizing the extracted features with a final number ofbits per field and adding the time stamp for the prepared vector. Thisvector may be used as the entry data for the human state classifier (forboth training and classifying stages).

Human State Classifier

The Human state classifier is a VQ (Vector Quantization) basedclassifier. This classifier consists of two main phases: (i) Thetraining phase is carried out offline (supervised training) and online(unsupervised training), where a stream of features vectors reflectingvarious states are used as a preliminary database for vectorquantization and finding the set of code-vectors (centroids) thatsufficiently representing the instantaneous human states. The set of thecalculated code-vectors are called codebook. Some embodiments of thetraining sessions are provided in more details hereinafter. (ii) Theclassifying phase is executed during the online operation while anunknown features vector is entered into the classifier and theclassifier determines what the most probable state that it represents.The classifier output is the determined states and the set of themeasured statistical distances (probabilities), i.e., the probability ofState-i given the observation-O (the features vector). Theaforementioned probability scheme may be formulated by: P (Si|O). Thedetermined instantaneous state is called “Local Decision”. The VQ statesare defined as the set of instantaneous states at various locations atthe monitored home environment. Therefore, any state is a twodimensional results which is mapped on the VQ state matrix. (iii) TheState matrix consists of the state (row) and location (Column) followedby a time stamp. Typical elderly home environment consists of thespecific locations (Primary zones) and others non-specified locations(Secondary zones). State is defined as the combination of posture/motionat a specific location (e.g., S21 will indicate sleeping at Bedroom).

FIG. 6A is a table 134 illustrating an exemplary states definition inaccordance with some embodiments of the present invention. FIG. 6B is atable 135 illustrating an exemplary states matrix in accordance withsome embodiments of the present invention.

Cognitive Situation Analysis (CSA)

The CSA's objective is to recognize the abnormal human patternsaccording to a trained model that contains the possible abnormal cases(e.g., fall). The core of the CSA, in this embodiment, may, in anon-limiting example a Hidden Markov Model (HMM) based patternrecognition. The CSA engine searches for states patterns that are taggedas an emergencies or abnormal patterns. These predefined patterns arestored in a patterns codebook. The output of the CSA is the Globalrecognized human situation.

FIG. 6C is a table 136 illustrating exemplary abnormal patterns inaccordance with some embodiments of the present invention. It can beseen that in the first abnormal case (Critical fall), it appears thatthe person was sleeping in the leaving room (S25), then was standing(S45) and immediately fell down (S65). He stayed on floor (S15) andstart being in stress due to high respiration rate (S75). The CSA maycontain additional codebook (irrelevant codebook) to identify irrelevantpatterns that might mislead the system decision.

Communication Unit

The communication unit creates the channel between the system and theremote caregiver (family member or operator center). It may be based oneither wired (Ethernet) connectivity or wireless (e.g., cellular or WiFicommunication or any other communication channel).

The communication unit provides the following functionalities: (i) Thisunit transmits any required ongoing situation of the monitored personand emergency alerts. (ii) It enables the two way voice/videocommunication with the monitored person when necessary. Such acommunication is activated either automatically whenever the systemrecognizes an emergency situation or remotely by the caregiver. (iii) Itenables the remote system upgrades for both software and updatedcodebooks (as will be in further detail below). (iv) It enables thecommunication to the centralized system (cloud) to share commoninformation and for further big data analytics based on multipledeployments of such innovated system.

FIG. 7 is a diagram illustrating cloud-based architecture 700 of thesystem in accordance with embodiments of the present invention. Raw datahistory (e.g., states stream) is passed from each local system 100A-100Eto the central unit located on a cloud system 710 and performs variousdata analysis to find correlation of states patterns among the multipleusers' data to identify new abnormal patterns that may be reflected justbefore the recognized abnormal pattern. New patterns code vectors willbe included to the CSA codebook and cloud remotely updates the multiplelocal systems with the new code-book. The data will be used to analyzedaily operation of local system 100A-100E.

FIG. 8 is a diagram illustrating a floor plan 800 of an exemplaryresidential environment (e.g., an apartment) on which the process forthe initial training is described herein. The home environment is mappedinto the primary zones (the major home places that the monitored personattends most of the time as bedroom 810, restroom 820, living room 830and the like) and secondary zones (the rest of the barely usedenvironments). The VQ based human state classifier (described above) istrained to know the various primary places at the home. This is doneduring the system setup while the installer 10A (being the elderlyperson or another person) stands or walks at each primary place such asbedroom 810, restroom 820, and living room 830 and let the system learnsthe “fingerprint” of the echo signals extracted features that mostlyrepresents that place. These finger prints are stored in the VQpositions codebook. In addition, the system learns the home externalwalls boundaries. This is done during the system setup while theinstaller stands at various places along the external walls and lets thesystem tune its power and processing again (integration) towards eachdirection. For example, in bedroom 810, installer 10A may walk alongwalls in route 840 so that the borders of bedroom 810 are detected bytracking the changes in the RF signal reflections throughout the processof walking. A similar border identification process can be carried outin restroom 820, and living room 830. Finally, the system learns toidentify the monitored person 10B. This is done by capturing thefingerprint of the extracted features on several conditions, such as (1)while the person lays at the default bed 812 (where he or she issupposed to be during nighttime) to learn the overall body volume, (2)while the person is standing to learn the stature, and (3) while theperson walks to learn the gait. All the captured cases are stored in theVQ unit and are used to weight the pre-trained codebooks and to generatethe specific home/person codebooks. According to some embodiments, oneor additional persons such as 20 can also be monitored simultaneously.The additional person can be another elderly person with specificfingerprint or it can be a care giver who needs not be monitored forabnormal postures.

FIG. 9 is a diagram illustrating yet another aspect in accordance withsome embodiments of the present invention. System 900 is similar to thesystem described above but it is further enhanced by the ability tointerface with at least one wearable medical sensor 910A or 910B coupledto the body of human 10 configured to sense vital signs of human 10, anda home safety sensor 920 configured to sense ambient conditions at saidspecified area, and wherein data from said at least one sensor are usedby said decision function for improving the decision whether an abnormalphysical event has occurred to the at least one human in said specifiedarea. The vital signs sensor may sense ECG, heart rate, blood pressure,respiratory system parameters and the like. Home safety sensors mayinclude temperature sensors, smoke detector, open door detectors and thelike. Date from all or some of these additional sensors may be used inorder to improve the decision making process described above.

FIG. 10 is a high level schematic flowchart of a method 600 according tosome embodiments of the invention. Method 600 may be at least partiallyimplemented by at least one computer processor. Certain embodimentscomprise computer program products comprising a computer readablestorage medium having computer readable program embodied therewith andconfigured to carry out of the relevant stages of method 600.

Method 600 may comprise transmitting UWB RF signals via transmittingantenna(s) at a specified area (such as an environment including atleast one human) and receiving echo signals via receiving antenna(s)(stage 500). At least one of the UWB RF transmitting and receivingantennas comprises a Synthetic Aperture Antenna Array (SAAA) comprisinga plurality of linear baseline antenna arrays (“baselines”). Method 600may comprise configuring receiving antenna(s) and/or transmittingantennas(s) as a Synthetic Aperture Antenna Array (SAAA), such asbaseline(s) (stage 510), for example, method 600 may compriseconfiguring the UWB RF receiving SAAA as a plurality of linear baselineantenna arrays arranged in a rectangle as a non-limiting example,possibly parallel to edges thereof or at acute angles to edges thereof(stage 520), e.g., as illustrated below in a non-limiting manner. Method600 may comprise designing at least one of the linear baseline antennaarrays to comprise two (or more) parallel metal beams flanking theantenna elements of the baseline, to widen the baseline's field of view(stage 525).

Method 600 may further comprise using multiple antennas to implementvirtual displacement of the baselines (stage 530), i.e., virtuallydisplacing transmitting or receiving baselines to enhance performance(stage 535). Method 600 may further comprise implementingphase-shifting-based integration (back-projection) to derive parametersrelating to the human(s) (stage 540), such as location, movement and/orposture features.

Method 600 may further comprise canceling environmental clutter (stage605), e.g., by filtering out static non-human related echo signals(stage 606), extracting from the filtered echo signals, a quantifiedrepresentation of position postures, movements, motions and breathing ofat least one human located within the specified area (stage 610),identifying a most probable fit of human current state that representsan actual human instantaneous status (stage 690) and applying a patternrecognition based decision function to the identified states patternsand determine whether an abnormal physical event has occurred to the atleast one human in the specified area (stage 693) (see additionaldetails below).

Method 600 may further comprise finding the best match to a codebookwhich represents the state being a set of human instantaneouscondition/situation which is based on vector quantized extractedfeatures (stage 691).

Method 600 may further comprise ensuring, by the filtering out, that nohuman body is at the environment, using static clutter estimation andstatic clutter subtraction (stage 607).

Method 600 may further comprise quantizing the known states featuresvectors and generating the states code-vectors (stage 692A), measuringthe distance between unknown tested features vectors and pre-definedknown code-vectors (stage 692B) and finding the best fit between unknowntested features vector and pre-determined code-vectors set, using themost probable state and the relative statistical distance to the testedfeatures vector (stage 692C).

Method 600 may further comprise generating the set of abnormal statespatterns as a reference codebook, a set of states transitionprobabilities, and a states-patterns matching function to find and alerton a match between a tested states pattern and the pre-defined abnormalpattern of the codebook (stage 694). Method 600 may further comprisecommunicating an alert upon determining of an abnormal physical event(stage 695).

Method 600 may further comprise estimating the reflected clutter from aspecific voxel to extract the human position and posture features (stage612A), extracting the human motions and breathing features using Dopplersignatures (stage 612B) and creating a quantized vectors of theextracted features (stage 612C).

Method 600 may comprise, with respect to posture (and position) featuresextraction 612A, processing the received echo signals to derive aspatial distribution of echo sources in the environment using spatialparameters of the transmitting and/or receiving antennas (stage 620),carried out, e.g., by a back-projection algorithm, and estimating aposture of the at least one human by analyzing the spatial distributionwith respect to echo intensity (stage 628). Method 600 may comprisecanceling environmental clutter by filtering out static non-humanrelated echo signals (see stages 605, 606).

Processing 620 may be carried out with respect to multiple antennabaselines as the transmitting and/or receiving antennas, as explainedabove. Method 600 may further comprise enhancing echoes received from alower level in the environment to enhance detection sensitivity to alaying posture of the at least one human (stage 622).

Method 600 may further comprise detecting a position of the at least onehuman from the spatial distribution (stage 624) and possibly trackingthe detected position over time. Canceling environmental clutter 605 maybe carried out by spatially characterizing the static non-human relatedecho signals during an absence of the at least one human from theenvironment, as detected by the tracking (stage 626).

Posture estimation 628 may comprise analyzing the spatial distributionusing curve characteristics of one or more projections of an intensityof the received echo signals onto at respective one or more axes (stage630), in particular with respect to one or more horizontal axis and avertical axis. The spatial distribution may be defined using voxels andthe posture may be estimated 628 using high-power voxels as defined by aspecified power threshold (stage 632).

Method 600 may further comprise classifying the posture characteristicsof the at least one human to indicate a state of the at least one human(stage 634), possibly by preparing at least one codebook during atraining phase and using the at least one codebook to classify thedetected postures (stage 636), as explained above. As explained below,Classification 634 may be carried out by identifying a most probable fitof one of a plurality of predefined states to the motion characteristics(stage 692C), possibly followed by generating an alert once theindicated state is related to at least one specified emergency (stage695), the alert generation being possibly based on pattern recognitionwith respect to previously indicated states (stage 694).

Method 600 may further comprise processing the received echo signals toyield a range-bin-based slow signal that is spatio-temporallycharacterized over a plurality of spatial range bins and a plurality oftemporal sub-frames, respectively (stage 640) and deriving from the slowsignal a Doppler signature and a range-time energy signature as motioncharacteristics of the at least one human (stage 650). Method 600 maycomprise deriving the Doppler signature by comparing spectral signaturesof sub-frames in the slow signals, which are related to identifiedhuman-related range bins and sub-frames (stage 642) and deriving theenergy signature by evaluating powers of the slow signal at identifiedhuman-related range bins and sub-frames (stage 644). Method 600 maycomprise deriving the Doppler signature and/or the energy signature withrespect to different body parts of the at least one human (stage 646).

Deriving 650 may further comprise deriving location data as movementcharacteristics of the at least one human (stage 652). Deriving of thelocation data 652 may comprise detecting displacements of the at leastone human using back-projection (stage 654), using the received echosignals to derive, by back projection, 2D location data and 3D posturedata about the at least one human (stage 655), and/or identifyinghuman-related range bins and sub-frames in the slow signals (stage 656).Deriving of the location data 652 may be carried out using aspatio-temporal histogram of the range-time energy signature and byidentifying on the histogram range changes of at least body parts (e.g.,limbs) of the at least one human (stage 658). The motion characteristicsand/or movement characteristics may comprise gait parameters.

Method 600 may further comprise handing over detecting 654 among aplurality of interferometry units according to detected displacements(stage 660), i.e., using different interferometry units for detection654 according to displacement parameters, such as coverage region,signal intensity etc., as explained below. Method 600 may be carried outby a plurality of UWB RF receiving SAAAs positioned at a plurality ofpositions, and may further comprise integrating the received echosignals from the UWB RF receiving SAAAs (stage 662).

Method 600 may comprise, after transmitting the UWB RF signals andreceiving the echo signals (stage 500) and after processing the receivedecho signals to yield the range-bin-based slow signal (stage 640),estimating at least one respiration parameter of the at least one humanby analyzing the slow signal (stage 665).

Method 600 may further comprise removing motion related components fromthe slow signal (stage 667) and deriving a range-bin power spectrumtherefrom to identify a respiration-related ROI (stage 668), possiblyidentifying chest(s) of the human(s) by detecting ROI cross-correlationas described above (stage 669), deriving from the respiration-relatedROI a respiration signal (stage 670), and extracting at least onerespiration feature from the respiration signal (stage 675). Deriving670 of the respiration signal may be carried out by combining,coherently, a plurality of differently phase-shifted slow signals (stage672) and/or with respect to a derivative of the respiration signal(stage 674).

The respiration features may comprise any of: a respiration rate,respiration asymmetry, respiration tidal volume changes, breath dutycycle parameters, inhalation and/or exhalation durations, maximalinhalation and/or exhalation values, and chest movements and relatedenergy, velocity and/or activity, as explained above.

Method 600 may further comprise applying a PCA (principal componentanalysis) on earlier-derived slow signals to estimate a respiration rate(stage 677) and/or applying a FFT (fast Fourier transform) to the slowsignal prior to range-bin power spectrum derivation 668, and enhancinghigh frequencies of the spectrum (stage 680), as demonstrated in detailabove.

Method 600 may further comprise deriving a location of the at least onehuman (and/or the chest of the human) prior to the derivation of therespiration signal and possibly tracking the detected position over time(stage 682). Other signal processing stages described above as well asclassification stages described below may be applied with respect to therespiration signal(s) and feature(s). Various types of features(movement, motion, posture and/or respiration) may be correlated and/orused in combination for the classification and alert generationdescribed below.

Method 600 may comprise classifying the position and/or posture and/ormotion and/or movement and/or respiration characteristics of the atleast one human to indicate a state of the at least one human (stage688). Classification 688, e.g., by identifying the most probable fit690, may be carried out by identifying a most probable fit of one of aplurality of predefined states to the motion characteristics.Classification 688 may comprise classifying respiration parameters andfeature to indicate respiration modes of the human(s), and may compriseusing the respiration modes to indicate the state of the human(s).

Communicating the alert 695 may be carried out by generating the alertonce the indicated state is related to at least one specified emergency.The alert generation may be based on pattern recognition with respect topreviously indicated states.

Aspects of the present invention are described above with reference toflowchart illustrations and/or portion diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each portion of the flowchartillustrations and/or portion diagrams, and combinations of portions inthe flowchart illustrations and/or portion diagrams, can be implementedby computer program instructions. These computer program instructionsmay be provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or portion diagram portion or portions.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or portiondiagram portion or portions.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/orportion diagram portion or portions.

The aforementioned flowchart and diagrams illustrate the architecture,functionality, and operation of possible implementations of systems,methods and computer program products according to various embodimentsof the present invention. In this regard, each portion in the flowchartor portion diagrams may represent a module, segment, or portion of code,which comprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that, in somealternative implementations, the functions noted in the portion mayoccur out of the order noted in the figures. For example, two portionsshown in succession may, in fact, be executed substantiallyconcurrently, or the portions may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each portion of the portion diagrams and/or flowchart illustration,and combinations of portions in the portion diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

In the above description, an embodiment is an example or implementationof the invention. The various appearances of “one embodiment”, “anembodiment”, “certain embodiments” or “some embodiments” do notnecessarily all refer to the same embodiments. Although various featuresof the invention may be described in the context of a single embodiment,the features may also be provided separately or in any suitablecombination. Conversely, although the invention may be described hereinin the context of separate embodiments for clarity, the invention mayalso be implemented in a single embodiment. Certain embodiments of theinvention may include features from different embodiments disclosedabove, and certain embodiments may incorporate elements from otherembodiments disclosed above. The disclosure of elements of the inventionin the context of a specific embodiment is not to be taken as limitingtheir use in the specific embodiment alone. Furthermore, it is to beunderstood that the invention can be carried out or practiced in variousways and that the invention can be implemented in certain embodimentsother than the ones outlined in the description above.

The invention is not limited to those diagrams or to the correspondingdescriptions. For example, flow need not move through each illustratedbox or state, or in exactly the same order as illustrated and described.Meanings of technical and scientific terms used herein are to becommonly understood as by one of ordinary skill in the art to which theinvention belongs, unless otherwise defined. While the invention hasbeen described with respect to a limited number of embodiments, theseshould not be construed as limitations on the scope of the invention,but rather as exemplifications of some of the preferred embodiments.Other possible variations, modifications, and applications are alsowithin the scope of the invention. Accordingly, the scope of theinvention should not be limited by what has thus far been described, butby the appended claims and their legal equivalents.

The invention claimed is:
 1. A method comprising: transmitting, via atleast one transmitting antenna, ultra-wide band (UWB) radio frequency(RF) signals at an environment including at least one human, andreceiving, via at least one receiving antenna, echo signals from theenvironment; processing the received echo signals to yield arange-bin-based slow signal that is spatially characterized over aplurality of spatial range bins; removing motion related components fromthe slow signal and deriving a range-bin power spectrum therefrom toidentify a respiration-related region of interest; deriving from therespiration-related region of interest a respiration signal; applying aPCA (principal component analysis) on earlier-derived slow signals toestimate a respiration signal; extracting the at least one respirationfeature from the respiration signal; and classifying the at least onerespiration feature to indicate a respiration mode of the at least onehuman.
 2. The method of claim 1, wherein the deriving of the respirationsignal is carried out by coherently combining a plurality of differentlyphase-shifted slow signals.
 3. The method of claim 1, wherein thederiving of the respiration features is carried out with respect to aderivative of the respiration signal.
 4. The method of claim 3, whereinthe respiration features comprise at least one of: a respiration rate,respiration asymmetry, respiration tidal volume changes, breath dutycycle parameters, inhalation and/or exhalation durations, maximalinhalation and/or exhalation values, and chest movements and relatedenergy, velocity and/or activity.
 5. The method of claim 1, furthercomprising identifying at least one respective chest of the at least onehuman by detecting cross-correlation in the respiration-related regionof interest.
 6. The method of claim 5, further comprising tracking aposition of the at least one respective chest over time.
 7. The methodof claim 1, further comprising applying a FFT (fast Fourier transform)to the slow signal prior to range-bin power spectrum derivation andenhancing high frequencies of the spectrum.
 8. The method of claim 1,wherein the processing is carried out with respect to a plurality ofantenna baselines as the at least one transmitting and/or receivingantennas.
 9. The method of claim 1, further comprising classifying therespiration mode of the at least one human to indicate a state of the atleast one human.
 10. The method of claim 9, wherein the classifying iscarried out by preparing at least one codebook during a training phaseand using the at least one codebook to classify the detected postures.11. The method of claim 9, wherein the classifying is carried out byidentifying a most probable fit of one of a plurality of predefinedstates to the respiration features.
 12. The method of claim 9, whereinthe classifying is carried out with additional respect to motion and/orposture features.
 13. The method of claim 9, further comprisinggenerating an alert once the indicated state is related to at least onespecified emergency and is based on pattern recognition with respect topreviously indicated states.
 14. A non-wearable monitoring systemcomprising: an ultra-wide band (UWB) radio frequency (RF) interferometerconfigured to transmit UWB RF signals at, and to receive echo signalsfrom, an environment including at least one human, a processing unitconfigured to: process the received echo signals to yield arange-bin-based slow signal that is spatially characterized over aplurality of spatial range bins; remove motion related components fromthe slow signal and derive a range-bin power spectrum therefrom toidentify a respiration-related region of interest; derive from therespiration-related region of interest a respiration signal; and apply aPCA (principal component analysis) on earlier-derived slow signals toestimate a respiration signal; a feature extractor configured toestimate at least one respiration parameter of the at least one human byanalyzing the respiration signal, and a human state classifierconfigured to classify at least one respiration feature of the at leastone human to indicate a respiration mode of the at least one human. 15.The non-wearable monitoring system of claim 1, wherein the processingunit is further configured to apply, prior to the range-bin powerspectrum derivation, a FFT (fast Fourier transform) to the slow signaland enhancing high frequencies of the spectrum; and to apply a PCA(principal component analysis) on earlier-derived slow signals toestimate a respiration signal.
 16. The non-wearable monitoring system ofclaim 1, wherein the processing unit is further configured to identifyat least one respective chest of the at least one human by detectingcross-correlation in the respiration-related region of interest.
 17. Thenon-wearable monitoring system of claim 14, wherein the human stateclassifier is further configured to classify the respiration mode of theat least one human to indicate a state of the at least one human, andfurther comprising an abnormality situation pattern recognition moduleconfigured to generate an alert once the indicated state is related toat least one specified emergency, wherein the classifying is carried outby preparing at least one codebook during a training phase and using theat least one codebook to classify the detected postures.